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FACTORS AFFECTING ADOPTION OF E- LEARNING
TECHNOLOGY IN KENYA
BY
KINGORI RHODA MBITHE
UNITED STATES INTERNATIONAL UNIVERSITY-AFRICA
SUMMER 2018
FACTORS AFFECTING ADOPTION OF E- LEARNING
TECHNOLOGY IN KENYA
BY
KINGORI RHODA MBITHE
A Research Project Report Submitted to the Chandaria School of
Business in Partial Fulfillment of the Requirement for the Degree of
Masters in Business Administration (MBA)
UNITED STATES INTERNATIONAL UNIVERSITY-AFRICA
SUMMER 2018
ii
STUDENTS DECLARATION
I, the undersigned, declare that this is my original work and has not been submitted to any
other college, institution or university other than the United States International
University-Africa for academic credit.
Signed: ________________________ Date: _________________________
Kingori Rhoda Mbithe (ID: 632538)
This project has been presented for examination with my approval as the appointed
supervisor.
Signed: ________________________ Date: _________________________
Dr. Joseph Ngugi Kamau
Signed: _______________________ Date: _________________________
Dean, Chandaria School of Business
iii
COPYRIGHT
All rights reserved. No part of this research proposal may be photocopied, documented ,
stored in recovery system or transferred in any means without prior permission of United
States International University-A or the author.
© Copyright by Kingori Rhoda Mbithe, 2018
iv
ABSTRACT
Internet penetration in Africa has been accelerated, with Kenya at the fore front at over
89.5% penetration, this penetration has led to an increase in knowledge repository for
Kenyans to choose from and as a result E-learning has steadily increased not only in
tertiary institutions but in informal learning as well. Facebook and YouTube are some of
the web based tools, – that are listed as having potential applications for teaching and
learning. As a result, it is essential to understand the challenges faced in the adoption of e-
learning technology. The overall purpose of this study was to investigate the factors
affecting adoption of e-learning technology in Kenya with a focus on three key factors,
self-efficacy, objective usability and system accessibility.
This study used explanatory/hypothesis research design to investigate the factors which
affect adoption of e-learning technology in Kenya. An explanatory research design was fit
to the study because it will helped to ascertain not only the relationship between the
different variables, but measure the effect and strength of each independent variable on
the dependent variable which was the technology adoption. The target population was
social media (Facebook) users in Kenya. According to Internet World Statistics there are
over 6.2 million Facebook subscribers in Kenya as of June 2017. This study focused on
200 social media users in Nairobi who were interested in e-learning or have ever learned
online. They were sampled by geographic cluster, simple random sampling technique
with a focus on Nairobi. Online questionnaire was used by Google form and 93%
responded.
In the first objective of self-efficacy, the correlation result revealed positive correlation
between perceived ease of use with self-efficacy (r=0.441, p<0.05) and perceived
usefulness with self-efficacy(r=0.337, p<0.05). On the CFA, there was a strong model
equation but on the SEM it was not. The path coefficient for the relationship between SE
and adoption of e-learning was weak. In the second objective, the correlation result
revealed positive correlation between perceived ease of use with objective usability
(r=0.598, p<0.05) and perceived usefulness with SE (r=0.456, p<0.05). Based on SEM,
the path coefficient for the relationship between objective usability and adoption of e-
learning was significant. Without any latent/intervening variable, OU and PEOU was
positive and significant at the 0.05 level (βeta=0.938, T-value =3.658, p<0.05). The
v
positive relationship indicates that one unit increase in OU will result in 0.938 increases
in PEOU.
In the last objective of system accessibility, the correlation result revealed positive
correlation between perceived ease of use with system accessibility (r=0.441, p<0.503)
and perceived usefulness with system accessibility (r=0.514, p<0.05). The path
coefficient for the relationship between system accessibility and adoption of e-learning
was weak and not significant hence dropped from the model.The research established that
Self-efficacy had little to no effect on adoption of e-learning technology in Kenya.
Objective usability was the strongest factor affecting the adoption of e-learning
technology while system accessibility had correlation but was not strong enough to
impact the model and as such impact the adoption of e-learning technology in Kenya was
weak. These findings provided some useful insights for governments, potential instructors
or educators, e-learning platforms and policy makers. This recommends and highlights
the need to shift from the metropolitan areas to the rural areas in order to get a holistic
view of the e-learning environment in Kenya.
This research shed light on objective usability as a strong factor on perceived ease of use
and perceived usefulness and as such on the adoption of e-learning technology in Kenya.
Based the responses received and the SEM analysis further research should focus on the
two variables of system accessibility and self-efficacy involving a larger number of
variables to examine, as well as larger numbers of respondents to support the factor
analysis. Research should also focus on attitude and behavioral intention and their
interaction with perceived eases of use and perceived usefulness. The research needs to
now shift from the metropolitan areas to the rural areas in order to get a holistic view of
the e-learning environment in Kenya.
vi
DEDICATION
This work is dedicated to my supportive family and supportive friends who have been my
accountability partners as I work on this thesis.
vii
ACKNOWLEDGEMENT
Over everything else I want to thank God for bringing me this far and keeping me in
health and joy as I pursue my life and this MBA. I would like to appreciate my brother
Kenneth Minire who has been my support both financially and emotionally through this
journey. I would also like to thank my mother and sister who have been my greatest
cheerleaders through my university education. I would also like to acknowledge friends
and the USIU fraternity who have enabled me to achieve my educational goals.
viii
TABLE OF CONTENT
STUDENTS DECLARATION ........................................................................................ ii
COPYRIGHT ................................................................................................................... iii
ABSTRACT ...................................................................................................................... iv
DEDICATION.................................................................................................................. vi
ACKNOWLEDGEMENT .............................................................................................. vii
TABLE OF CONTENT ................................................................................................. viii
LIST OF TABLES ........................................................................................................... xi
LIST OF FIGURES ........................................................................................................ xii
LIST OF ACRONMYS AND ABBREVIATIONS ..................................................... xiii
CHAPTER ONE ................................................................................................................1
1.0 INTRODUCTION ........................................................................................................1
1.1 Background of the Study ...............................................................................................1
1.2 Statement of the Problem ...............................................................................................5
1.3 General Objective ..........................................................................................................5
1.4 Specific Objectives ........................................................................................................6
1.5 Significance of the Study ...............................................................................................6
1.6 Scope of the Study .........................................................................................................6
1.7 Definition of Terms........................................................................................................7
1.8 Chapter Summary ..........................................................................................................7
CHAPTER TWO ...............................................................................................................9
2.0 LITERATURE REVIEW ............................................................................................9
2.1 Introduction ....................................................................................................................9
2.2 The Effect of Self-Efficacy on Adoption of E-learning Technology ............................9
2.3 The Effect of Objective Usability on Adoption of E-learning Technology .................13
2.4 The Effect of System Accessibility on Adoption of E-learning Technology ..............18
2.5 Chapter Summary ........................................................................................................23
ix
CHAPTER THREE .........................................................................................................24
3.0 RESEARCH METHODOLOGY .............................................................................24
3.1 Introduction ..................................................................................................................24
3.2 Research Design...........................................................................................................24
3.3 Target Population .........................................................................................................24
3.4 Sampling Design ..........................................................................................................25
3.5 Data Collection Methods .............................................................................................27
3.6 Research Procedure ......................................................................................................27
3.7 Data Analysis Methods ................................................................................................29
3.8 Chapter Summary ........................................................................................................30
CHAPTER FOUR ............................................................................................................31
4.0 RESULTS AND FINDINGS .....................................................................................31
4.1 Introduction ..................................................................................................................31
4.2 Response Rate ..............................................................................................................31
4.3 Demographic Characteristics .......................................................................................31
4.4 Descriptive Analysis of Study Variables .....................................................................34
4.5 Inferential Analysis ......................................................................................................37
4.6 Measurement Model ....................................................................................................38
4.7 Structural Estimation Model (SEM) ............................................................................46
4.8 Regression Weights .....................................................................................................50
4.9 Predictive Relevance of the Model ..............................................................................51
4.10 Chapter Summary ......................................................................................................51
CHAPTER FIVE .............................................................................................................53
5.0 DICUSSION, CONCLUSIONS AND RECOMMENDATIONS. .........................53
5.1 Introduction ..................................................................................................................53
5.2 Summary of the Study .................................................................................................53
5.3. Discussion of the Results ............................................................................................55
5.4. Conclusions .................................................................................................................59
5.5 Recommendations ........................................................................................................60
x
REFERENCES .................................................................................................................62
APPENDICES ..................................................................................................................71
Appendix I: Questionnaire ..............................................................................................71
xi
LIST OF TABLES
Table 3.1: Clusters………………………………………………………………………..26
Table 4. 1: Response rate ...................................................................................................31
Table 4. 2 Descriptive of Independent Variables ...............................................................35
Table 4. 3 Descriptive of Latent Variables ........................................................................36
Table 4. 4 Descriptive of Dependent Variables .................................................................37
Table 4. 5 Normality Test Using Skewness and Kurtosis Statistics ..................................38
Table 4. 6 KMO and Bartlett's Test ...................................................................................39
Table 4. 7 Total Variance Explained .................................................................................39
Table 4. 8 Communalities and Pattern Matrix ...................................................................42
Table 4. 9 Model Fits for CFA Model ...............................................................................44
Table 4. 10 Construct Reliability .......................................................................................44
Table 4. 11 Inter-Item Correlation Matrix .........................................................................45
Table 4. 12 Correlation Coefficient ...................................................................................46
Table 4. 13 Model Fits for Structural Model .....................................................................47
Table 4. 14 Standardized Residual Covariances (Group number 1 - Default model) .......48
Table 4. 15 Model fits for Structural Model ......................................................................50
Table 4. 16 Regression Weights ........................................................................................51
xii
LIST OF FIGURES
Figure 4.1 Gender ..............................................................................................................32
Figure 4.2 Age Bracket ......................................................................................................32
Figure 4.3 Level of Education ...........................................................................................33
Figure 4.4 Marital Status....................................................................................................33
Figure 4.5 Online Earning..................................................................................................34
Figure 4.6 Confirmatory Factor Analysis Model for Study Variables ..............................43
Figure 4.7 Structural Model for the Relationship of the Study Variables .........................46
Figure 4.8 Improved Structural Model for the Relationship of the Study Variables .........49
xiii
LIST OF ACRONMYS AND ABBREVIATIONS
PEOU Perceived Ease of Use
PU Perceived Usefulness
OU Objective Usability
SE Self Efficacy
SA System Accessibility
SD Standard Deviation
CFA Confirmatory Factor Analysis
1
CHAPTER ONE
1.0 INTRODUCTION
1.1 Background of the Study
E-learning can be defined as the use of computer network technology, primarily over an
intranet or through the Internet, to deliver information and instruction to individuals
(Welsh, Wanberg, Brown, & Simmering, 2003). Since the 1960s, e-learning has evolved
in different ways in business, education, the training sector, and the military and currently
means different things in different sectors. In the tertiary- school sector, ‘e-leaning’ refers
to the use of both software-based and online learning, whereas in business, higher-
education, the military and training sectors, it refers solely to a range of on-line
practices(Campbell, 2004). E-learning has evolved over the years as technology briskly
changes and upgrades; knowledge has become accessible to everyone around the world as
long as they have a device and internet. It has created accessibility in learning for even
those who have either visual or hearing impairment or disability, Batanero et al. (2014)
reflected on e-learning platforms and objects having visual, audio and text variations
adapted to different student needs including those with disability.
Along the global stage there has been a lot of research and documentation on the adoption
of e-learning in tertiary institutions because that was the first area in which adoption of
technology helped enhance the way in which instructors teach and students learn.
Numerous studies have shown that learning could be easier and more efficient, if it is
supported by ICT and above all if hypermedia systems and applications are available
(Debevc et al., 2007). As awareness continues Western and European tertiary institutions
and research institutes are investigating adoption to a deeper level, not only focusing on
adoption as it is, but the ways in which to improve adoption of this new form of
pedagogy. Tarus, Gichoya and Muumbo (2015) considered a shift in pedagogy necessary
especially for e-learning adoption, Costabile et al. (2005) concluded that in order to go
deeply into aspects concerning pedagogical approach and content semantics, experts of
education science and domain experts are to be involved.
As university administrators continue investing in information and communication
technologies (ICTs) such as the Internet, learning management systems (LMS) (Macharia
& Nyakwende, 2009), students and individuals are briskly turning to social media
platforms and alternative e-learning platforms to learn informal or life skills. According
2
to Bosch (2009), if one considers the large numbers of students on Facebook often
actively participating in discussions and groups, it cannot be ignored as a potential
educational tool- compared to university course sites. This introduces the aspect of m-
learning, a variation of e-learning but accessed through the mobile phones (Brown, 2003).
Because of affordability of devices, the uptake of m-learning especially from social media
sites has increased drastically. Brown (2003) presented on the role of m-learning in
Africa. In this research he highlighted that adoption of mobile technology in Africa is one
of the highest in the world ergo, the usability component is strong in m-technology and
the potential for the meshing of online learning in mobile will be efficient and effective
with great potential for adoption.
In the west (Europe and the America’s) three years ago, engaging academics in the use of
technologies in education was a relatively new priority within the higher education sector
(King & Boyatt, 2014) and this priority has only increased over the next three years. The
rise of informal learning platforms like Udemy.com, Lynda.com among others has shifted
the perception of e-learning from just tertiary education. These online learning platforms
as well as social media sites like Facebook and YouTube have dramatically increased the
pool of knowledge people can draw from and adoption of e-learning technology has
steadily increased, even if the untapped potential remains.
As we narrow down into Africa, several studies focus on the adoption of e-learning in
tertiary institutions. Though adoption is slower, Africa is not getting left behind in this
age of e-learning. According to Buabeng-Andoh (2012) realizing the effect of ICT on the
workplace and everyday life, today’s educational institutions try to restructure their
educational curricula and classroom facilities, in order to bridge the existing technology
gap in teaching and learning. As this gap continues to close, Africa is briskly adopting
social media sites and alternative e-learning platforms due to the increase in internet
penetration, with countries like Kenya, Ghana and Nigeria at the fore front (Internet
world Stats, 2017). In Kenya e-learning platforms like AMI (Africa Management
Institute) and Zydii.com are slowly gaining momentum and users, following briskly after
tertiary e-learning. The e-learning system acceptance has gained enormous attention by
the higher education institutions in developed countries. However, developing countries
are still lacking to reap maximum benefits of the cutting edge technology (Rehman,
2014). Due to the undoubted benefits, e-learning is gaining popularity in the developing
3
countries as well. However, developing countries are still at the rudimentary stage of e-
learning adoption
As e-learning is embraced across the globe there are several factors which affect the
adoption of technology in education. Most of these factors vary based on the focus of
study but all are reviewed under the context of the TAM (Technology Acceptance
Model). TAM is a model developed by Fred D. Davies Junior in 1989 which helped
people research and understand the effect of external factors on Perceived Ease of Use
(PEOU) and Perceived Usefulness (PU) of the technology (Davies, 1989). The PEOU and
PU affect the attitude toward use of the tech and as a result the actual system use. This
has become an essential standard in the research on adoption technology. This model is
readily adopted because external factors can be contextualized to an organization,
community or country.
Davies (1989) narrowed down the focus and did research on the determinants of his
model in his paper on ‘Perceived usefulness, perceived ease of use, and user acceptance
of information technology’. In this research he was able to determine the perceived ease
of use and perceived usefulness is strongly correlated to current usage of technology.
Venkatesh and Davies (1997) worked on experimenting and creating a viable tool to
discover and accurately measure the determinants of adoption of technology.
Most, if not all of the studies which focus on the adoption of e-learning technology utilize
TAM or a revised model to understand the factors which affect adoption of e-learning.
Different papers and research identified a variance of factors affecting the perception of
technology. King and Russell (2014), identified institutional infrastructure, staff attitudes
and skills, and perceived student expectations as factors affecting adoption of e-learning,
in Nigeria, Urhiewhu and Emojorho (2015) identified several factors like epileptic power
supply; non-availability of online databases; inadequate number of computers to access
digital information resources; inadequate bandwidth; Network problems among others. In
Kenya it emerged that implementation of e-learning faces a number of challenges which
include but are not limited to inadequate ICT, e-learning infrastructure, financial
constraints. Expensive and inadequate Internet bandwidth, lack of operational e-learning
policies, lack of technical skills on e-learning and e-content development by teaching
staff, among others (Tarus, 2011) were also factors which arose. Factors like affordability
of technology, internet penetration, electricity and access are some of the issues which
4
cause adoption of e-learning technology to be lower in East Africa in comparison to the
western world (Hennessy , et al., 2010)
From 2014 onwards, the research evolved and no longer just focused on infrastructure
and accessibility but factors like self- efficacy, attitude and objective usability. This can
be contributed to the rapidly increasing internet penetration in Africa, thus the
infrastructure (especially in terms of bandwidth) becomes more stable and the access to
internet makes people more comfortable with online content. Zainab et al. (2015) carried
out a study on e-training adoption in the Nigerian civil service and discovered that the
combined role of perceived cost, computer self-efficacy, technological infrastructure,
internet facilities, power supply, organizational support, technical support and
government support is critical for e-training adoption in developing countries, particularly
in Nigeria. Kiget, Wanyembi and Peters (2014) evaluated the usability attributes of user-
friendliness, learnability, technological infrastructure and policy. Their work highlighted
the fact that while adoption is key, efficient adoption is more important. Adoption is one
thing but continues use is essential, if usability of e-learning system is bad, learners fail in
their attempt to use the system (Kiget et al., 2014).
The evolution of the research into adoption of e-learning technology is a reflection of the
increased use of these systems in tertiary institutions among others. This is essential
because the narrative changes and a need to understand latent as well as external variables
rises and the contribution to the field becomes more viable.
The common denominator in terms of external factors affecting e-learning especially in
Africa is the infrastructure, technology, bandwidth and in tertiary institutions, pedagogy.
The literature is incredibly built around tertiary institutions and the technology adoption
model (TAM) (Davies, 1989), this creates great context when trying to understand e-
learning adoption, but there is still a gap in understanding the adoption of e-learning
especially in regards to individuals. Even though decisions about integration of
technology are commonly at a higher level, such as a school or tertiary level, it is the
individuals’ adoption patterns that illustrate a successful implementation. Therefore, it is
essential to understand such aspects of the process (Straub, 2009), why does an individual
choose to adopt a technology, especially if it is not a tertiary requirement?
5
1.2 Statement of the Problem
There have been various studies done on adoption of technology enhanced learning and
the factors which affect its adoption. According to Butler and Martin (2002), who
examined the factors affecting the adoption of technology in teaching and learning; the
findings revealed that many factors affect the rate of adoption, including an innovations
characteristic and various economic sociological, organizational and psychological
variables. According to Tarus (2011), implementation of e-learning is still at the infancy
stage in most Kenyan public universities due to many challenges related to
implementation. These challenges range from technological, organizational and
pedagogical challenges. In Tarus, Gichoya and Muumbo (2015) it emerged that
implementation of e-learning in Kenya faces a number of challenges which include but
are not limited to inadequate ICT and e-learning infrastructure, financial constraints,
expensive and inadequate Internet bandwidth, lack of operational e-learning policies, lack
of technical skills on e-learning and e-content development by teaching staff, lack of
interest and commitment among the teaching staff, and longer amount of time required to
develop e-learning courses.
Most studies in Kenya and internationally focus on the factors affecting e-learning within
the environment of schools. Tarus et al. (2015) focused on Moi University, a tertiary level
school, where as Butler simply focuses on tertiary universities like Illinois State
University (Sellbon & Butler, 2002). Whereas these findings give the researcher context it
raises the question of the gap for research toward adoption for e-learning technology in
Kenya for individuals and not just within tertiary institutions. With Kenya’s internet
penetration at 89.5% (Internet world Stats, 2017) even those without formal/tertiary
education have access to soft skills and variety knowledge and e-learning platforms.
This study investigated the factors affecting adoption of e- learning in Kenya with a focus
on informal education. The variables which the study focused on based on the context of
previous works and the gaps this research needs to fill were self-efficacy, objective
usability and system accessibility.
1.3 General Objective
The purpose of the study was to investigate the factors affecting the adoption of e-
learning technology in Kenya.
6
1.4 Specific Objectives
1.4.1 To establish the effect of self-efficacy on adoption of e-learning technology in
Kenya
1.4.2 To find out how objective usability affects adoption of e-learning technology in
Kenya
1.4.3 To investigate the effect of system accessibility on adoption of e-learning
technology in Kenya.
1.5 Significance of the Study
1.5.1 Governments
This study was significant in providing government with clarity in areas of improvement
and training for youth development and how they can make e-learning more accessible
for a well-rounded generation.
1.5.1 Potential Instructors
This was significant in aiding instructors and educators on the format and style required
for content creation and user interface taking into consideration the infrastructure (or
hardware) and related variables.
1.5.2 E-learning Platforms
This study was significant for upcoming platforms to mold their strategy to fit the African
context and the predominant variables affecting adoption.
1.5.3 Policy Makers
Policy makers will be able to recognize areas of improvement and subsidizing strategies
to help in the adoption of e-learning technology in Kenya.
1.6 Scope of the Study
This study focused on Nairobi Kenya which has a large percentage of youth who have a
higher probability of utilizing the internet and be exposed to the new wave of e-learning
in both tertiary and informal sectors.
7
1.7 Definition of Terms
1.7.1 E-learning
E-learning can be defined as the use of computer network technology, primarily over an
intranet or through the Internet, to deliver information and instruction to individuals
(Welsh et al., 2003).
1.7.2 Technology Acceptance Model
TAM is an information systems theory that models how users come to accept and use
technology (Davies, 1989).
1.7.3 Perceived Ease-Of-Use (PEOU)
Davies defined this as the degree to which a person believes that using a particular system
would be free from effort (Davies, 1989).
1.7.4 Computer Self Efficacy
Individuals' beliefs about their abilities to competently use computers in the determination
of computer use (Compeau & Higgins, 1995).
1.7.5 Objective Usability
Objective usability concerns aspects of the interaction not dependent on users’
perception; on the contrary these measures can be obtained, discussed, and validated in
ways not possible with subjective measures (Hornbaek, 2006).
1.7.6 System Accessibility
System accessibility refers to a situation where anyone, regardless of their personal
characteristics and type of environment, is able to access the information provided
through the Learning Objects (LO) (Batanero, Karhu, Holvikivi, Oton, & Amado-
Salvatierra, 2014).
1.8 Chapter Summary
E-learning is revolutionizing not only tertiary education but informal education as well.
The adoption of e-learning in formal education has undergone growth, but has not lacked
challenges, some of which are – pedagogy, technology, infrastructure, financial
8
constraints and bandwidth (especially as we narrow down into Africa). There has been a
variety of research on e-learning in tertiary education and this creates context and a
foundation to help fill the gap on adoption of e-learning technology. My research sought
to fill the gap by focusing on the determinants in the TAM (Technology Acceptance
Model). The scope of this research was Nairobi Kenya and the research was mixed
methodology of literature review and data collection. It is essential to note that the
Technology Adoption Model by Davies (1989) was the model of choice and was utilized
to analyze adoption of e-learning technology. The following chapter is the literature
review on the three factors affecting adoption of technology; computer self-efficacy,
objective usability and system accessibility.
9
CHAPTER TWO
2.0 LITERATURE REVIEW
2.1 Introduction
This chapter reviewed existing literature that identifies the three variables which this
research focused on; computer self-efficacy, objective usability and system accessibility.
Each of the factors elaborated on represents a specific objective of the study.
2.2 The Effect of Self-Efficacy on Adoption of E-learning Technology
Self-efficacy, also referred to as personal efficacy, is confidence in one's own ability to
achieve intended results (Omrod, 2008). This theory has its roots in the school of
psychology it basically explains a person’s belief in their own capacity, do they believe
they can accomplish the task? Albert Bandura defined self-efficacy as one's belief in one's
ability to succeed in specific situations or accomplish a task (Bandura, 1997). He believed
that one's sense of self-efficacy can play a major role in how one approaches goals, tasks,
and challenges. Whereas his work focused on human social behavior, it left the gap for
the application of the Social Cognitive Theory in the adoption of technology. According
to Schwarzer and Luszczynska (2005), self-efficacy influences the effort one puts forth to
change risk behavior and the persistence to continue striving despite barriers and setbacks
that may undermine motivation.
As we continue to understand self-efficacy we must now begin to understand it as it
conforms to the world of Technology. There is quite a bit of literature as it pertains to the
‘digital age’. Compeau and Higgins (1995) described computer-efficacy as individuals'
beliefs about their abilities to competently use computers in the determination of
computer use. They investigated how computer self- efficacy affected the adoption and
use of computers for a Canadian company. In this research computer self-efficacy was
found to exert significant influence on individuals' expectations of the outcomes of using
computers, their emotional reactions to computers (affect and anxiety), as well as their
actual computer use (Compeau & Higgins, 1995).This study remains relevant as the
adoption of computers – which are a channel for e-learning- is still lagging in the African
context. In this literature they also referenced Hill, whose research not only covered the
adoption use of computers but the use of this technology to enroll for computer courses
(Hill, Smith, & Mann, 1987).
10
Venkatesh and Davies (1997) narrowed down the Technology Adoption Model by
focusing on one of the main determinants of adoption; Perceived ease of use. In this
literature they conducted three experiments to determine if Computer self-efficacy,
among the three other determinants actually affected the PEOU (perceived ease of use),
which heavily affects adoption. Data from the three experiments spanning 106 subjects
and six different systems supported the hypothesis that an individual’s ease of use is
anchored to her or his general computer self-efficacy at all times (Venkatesh & David,
1997). This research was incredibly important because it helped to create an accurate tool
to test PEOU as well as highlighting the need to improve computer self- efficacy and aid
in technology adoption.
According to Straub (2009), individuals construct unique yet malleable perceptions of
technology that influence their adoption decisions. As he looked at adoption of
technology, he utilized not only the TAM model but, Rogers’s innovation diffusion
theory, the Concerns-Based Adoption Model, and the United Theory of Acceptance and
Use of Technology. As he discussed TAM, he asked an essential question ‘does perceived
ease of use equal self-efficacy?’ this contributed greatly to the literature because where as
he did not dismiss the relationship between perceived ease of use and self-efficacy he
clearly outlined the distinction. He highlighted that perceived ease of use is a judgment
about the qualities of a technology, but self-efficacy is a judgment about the abilities of an
individual (Straub, 2009).
According Hsia, Chang and Tseng (2012) most high-tech firms have implemented e-
learning systems to effectively train and up skill employees with practical and valuable
knowledge in order to sustain competitive advantage in the global competitive
environment, they cannot afford to be lax. They analyzed technology adoption using
locus of control and computer self- efficacy. In their work they emphasized that less
research has integrated the two control-related personality traits into one model to
understand their effect on user acceptance of e-learning (Hsia, Chang, & Tseng,
2012).One of their main hypothesis was; compared to individuals with low computer self-
efficacy, those with high computer self-efficacy tend to use IT more frequently and are
more likely to perceive IT use as effort-free(Compeau & Higgins, 1995). In the literature
and research, it was discovered that computer self-efficacy is an antecedent of perceived
ease of use and behavioral intention to use e-learning system (Hsia, Chang, & Tseng,
2012).
11
Saade and Kira (2009) investigated the relationship between anxiety and perceived ease
of use, perceived usefulness and how computer self- efficacy affects this relationship
within the context of e-learning. The research is incredibly relevant in that it creates a
verifiable relationship between anxiety and computer self-efficacy; use of technology
sometimes has unpleasant side effects, which may include strong, negative emotional
states that arise not only during interaction but even before, when the idea of having to
interact with the computer begins (Saade & Kira, 2009). In the end Saade and Kira (2009)
concluded that analysis results seem to suggest that computer self-efficacy does play an
important role in mediating the anxiety-perceived ease of use relationship for learning
management system (e-learning) usage or adoption.
Alenezy, Karim and Veloo (2010) also investigate the role of computer anxiety, computer
self-efficacy and a new angle of internet experience on influencing students to use e-
learning. Their scope is Saudi Arabia. This research adds value by tying together not only
the computer self- efficacy but the internet, which is an essential element in e-learning.
The authors build from the Technology Acceptance Model (Davies, 1989), the empirical
breakdown is very efficient and contributes greatly to the literature. The research proved
the hypothesis that computer self-efficacy influences students’ intention to use E-learning
while the hypothesis that internet experience will influence students’ intentions was
rejected (Alenezi, Abdul Karim, & Veloo, 2010).
Tarhini, Hone and Liu (2013) analyzed the factors affecting student’s acceptance of e-
learning environments or web based learning in developing countries with a focus on
Lebanon. This literature contributed significantly due to the fact that they utilized the
Technology Acceptance model (Davies, 1989). They did not specifically address Self-
efficacy but the model utilized to quantitatively measure PEOU and PU were molded by
self-efficacy questions. And they concluded that perceived ease of use directly affects
students’ behavioral intention to adopt certain technology (Tarhini, Hone, & Liu, 2013).
Hayashi et al. (2004) had some very interesting insight as they focused not only on the
adoption of e-learning technology, but the aspect of continuous learning and usage of the
system. Their main hypothesis was that success of an e-learning program in information
technology (IT) may require users to be equipped with a certain degree of computer self-
efficacy and affect for information systems. These factors may, in turn, influence the
satisfaction level of online learners and their intention to continue using the e-learning
12
system (Hayash et al., 2004). In a conclusion similar to Zainab et al. (2015) they
concluded that as a moderating factor, computer self-efficacy does not have significant
influence on learning outcomes.
Boateng et al. (2016) investigated the determinants of E-learning adoption in developing
countries, they theorized that findings from developed countries on ELA (E-Learning
Adoption) cannot be used as a basis for developing countries. Their scope was the
adoption of the E-learning in the University of Ghana and they focused on the
relationship between computer self-efficacy (CSE), perceived ease of use (PEOU),
perceived usefulness (PU) and attitude towards use (ATTU). Their study revealed that
CSE had a direct effect on PEOU. However, a test conducted between CSE and ATTU
was found not to be significant (Boateng, Mbrokoh, Boateng, Senyo, & Ansong, 2016).
This was in contrast to several studies by Sam et al. (2005) and Zhang and Espinoza
(1998) among others whose hypothesis proves that self-efficacy has a positively direct
influence on attitude towards use. This definitely contributed greatly to the literature due
to the contrasting result.
Zainab et al. (2015) carried out a study on e-training adoption in the Nigerian civil
service. The research highlights e-learning within the context of business and informal
training, taking a different direction to the usual e-learning in the formal education
system. The researchers discovered that the combined role of perceived cost, computer
self-efficacy, technological infrastructure, internet facilities, power supply, organizational
support, technical support and government support is critical for e-training adoption in
developing countries, particularly in Nigeria (Zainab, Bhatti, Pangil, & Battour, 2015).In
their research they referenced Lee (2006) who showed that mandatory usage of electronic
learning system is necessary in technology adoption- in order to build computer self-
efficacy. Furthermore, he also observed that computer self-efficacy has a significant
influence on technology adoption (Ching, 2006).
Zainab, Bhatti and Alshagawi (2017) investigated computer self-efficacy and the
technology acceptance model (TAM) constructs have in e-training adoption in the
Nigerian civil service. This was an interesting contribution because of the referral of e-
learning specifically as e-training. In their research they worked to determine the effects
of computer-self efficacy as well as other TAM constructs on perceived ease of use and
perceived usefulness. In their research they concluded that Computer self-efficacy was
statistically insignificant through PEOU, this differed from the conclusions of earlier
13
research by Zainab et.al (2015). In addition, PEOU had an indirect effect through PU.
Therefore, only PU of the TAM constructs indicated strong predictive strength in e-
training adoption (Zaniab, Bhatti, & Alshagawi, 2017).
Lwoga and Komba (2015) investigated not only the initial usage of web based learning
but the continued e-learning usage in Tanzania. This literature contributes greatly to
understanding perceived ease of use and perceived usefulness as it relates to East Africa.
The results showed that actual usage was determined by self-efficacy, while continued
usage intentions of web-based learning system was predicted by performance expectancy,
effort expectancy, social influence, self-efficacy, and actual usage (Lwoga & Komba,
2015). Within the same line as Macharia and Nyakwende (2009) highlighted other
challenges for e-learning adoption as; infrastructure barrier and weak ICT policies. They
also highlighted LMS user interface which was not user friendly and technical support,
limited skills, lack of awareness, resistance to change, and lack of time to prepare e-
content and use the e-learning system (Lwoga & Komba, 2015).
Macharia and Nyakwnde (2009) studied the link between external/ environmental factors
and the Technology Acceptance Model by Davies (1989) in the Kenyan higher education
context. This research contributed greatly to the literature because it emphasized the
importance of PEOU (perceived ease of use) and PU (perceived usefulness) while
outlining certain gaps; ‘TAM’s belief constructs are only partial mediators of the effects
of environmental differences. Perhaps most importantly, they underline the potential for
greater explanations of usage behavior when one is considering the direct effects of
external variables’ (Macharia & Nyakwende, 2009). This clearly justifies digging into
self- efficacy which is a behavioral determinant of Perceived Ease of Use.
2.3 The Effect of Objective Usability on Adoption of E-learning Technology
There are two types of usability measures in technology; the subjective and objective
usability. This section will focus on the literature around objective usability. According to
Hornbaek (2006), objective usability concerns aspects of the interaction not dependent on
users’ perception; on the contrary these measures can be obtained, discussed, and
validated in ways not possible with subjective measures. Basically, objective usability is
independent of user experience and this can be measured to give a broader and clearer
picture. Objective usability is one of the antecedents of PEOU -perceived ease of use
(Venkatesh & David, 1997), ergo it is essential to understand how it affects the adoption
14
of technology as it contributes to the technology acceptance model – TAM by Davies
(1989).
Venkatesh and Davies (1997) worked on testing perceived ease of use of technology
utilizing three antecedents, with objective usability being one of them. They theorized
that ease of use will be affected by objective usability of a specific system, but only after
direct hands on experience with the system (Venkatesh & David, 1997).This work
contributes greatly to the literature in that it not only identifies objective usability but also
experiments on the effect of direct experience on objective usability. Their experiment for
objective usability was applied utilizing Card, Moran and Newell (1980) Key Stroke
Model. This model measures how well as system allows one to perform a task and this is
predominantly measured by the time taken to achieve a certain task. Their research
presented that when users come into contact with systems which have an objective
usability that is lower than their own computer-self efficacy they are more likely to reject
the system (Venkatesh & David, 1997).
Zaharias and poulymenakou (2006) studied the essential relationship and interplay
between usability and instructional design. In this literature they highlight the factor of
usability with a focus not only on the system, but the characteristics. According to them
the increasing diversity of the learners’ population, the broad scope of their learning
needs, the decreasing tolerance of learners’ frustration and the diversity of their learning
tasks, impose that the human-centered design paradigm must be applied in e-learning
design (Zaharias & Poulymenakou, 2006). This mirrors the epithet of Venkatesh and
Davies (1997) who mirrored the sentiment that there should be a focus on both the system
and the system characteristics. The researchers focused on a case study of e-learning
design and implementation in the organizational context and not tertiary. The emphasis
was on learner -centered design and there was no clear distinction between objective and
subjective usability, simply an allusion in the descriptions and the experimentation tools
utilized.
Park and Wentling (2007) once again lump objective usability into computer usability,
the separation of the terms is alluded to, as subjective usability is discussed under
attitudes (computer self-efficacy and attitudes) perceived ease of use and perceived
usefulness. In this case it was safe to assume that computer usability referred to objective
usability. Their research intended to explore the relationship between the factors
15
associated with e-learning, particularly computer attitudes, computer usability, and
transfer of training in workplace learning (Park & Wentling, 2007). In their work
usability can be defined as the degree to which learners are able to easily and effectively
use the computer learning system to perform learning tasks (Carey, 1991; Shackel, 1997).
They concluded that usability has a significant direct effect not only on adoption but
transfer of training from what they learn.
As one reviews the work of Chin, Tsui, Lee (2016), one begins to notice the trend in
literature from 2005 onwards, phrases like design factors, usability design guidelines and
knowledge bases become more prominent. The focus shifts from usability with the basis
of the technology to a focus on usability in terms of design and characteristics. In this
research they argue that usability properties are fundamental to the design of e-learning
systems, and should be regarded as a means not only to scale e-learning systems to wider
contexts, but also to customize the design of learning activities so that improvements can
be made in a well-rounded manner (Chin, Tsui, & Lee, 2016). The research was applied
research and results aided in creating an interactive e-learning platform for the almost
isolated Bario tribe in Malaysia. This helped light the way discovering relevant research
on usability of e-learning platforms.
Costabile et al. (2005) contributed to this literature greatly, as their research focused not
only the effect of usability on e-learning adoption, but the necessity to measure it.
According to them the design of its interface should take into account the way students
learn and also provide good usability so that student’s interactions with the software are
as natural and intuitive as possible. The issue of pedagogy and its connection to the
usability was also highlighted. This was similar to the research by Tarus, Gichoya and
Muumbo (2015) which considered a shift in pedagogy necessary especially for e-learning
adoption. They concluded that in order to go deeply into aspects concerning pedagogical
approach and content semantics, experts of education science and domain experts are to
be involved (Costabile et al., 2005). E-learning adoption is not only about the usability of
the system, but the usability of the content as well.
According to Shackel (2009) as computers become cheaper and more powerful, it seems
certain that usability factors will become more and more dominant in the acceptability
decisions made by users and purchasers. In this research Shackel highlights the
16
importance not only of the objective computer usability but of the design and user
interface. He set his basis from Nicholls (1979):
The ‘‘end” user at the ‘‘terminal” was often the last person to be considered in the
design of the system. It is important to develop a new view of computing systems,
and to look at the user in a different light . . . taking this view of computing, the
center of a system is the user.
He highlighted the usability variables as; effectiveness, learnability, flexibility and
attitude (Shackel, 2009). He concludes by determining that while defining and evaluating
usability is important it must be done thoroughly and skillfully if good design for
usability is to be achieved.
Torun and Tekedere (2015) not only looked at adoption e-learning technology and the
usability, but looked closely at the technical aspects of the system usability. In their work
one of the most relevant literature focused on Human Computer Interaction (HCI) and
usability. They concluded that the ‘user’ factor must be taken into consideration while
devising a system, because when a system is being developed, the fact that the system in
question has been arranged to comply with the user is of importance in terms of the
functionality of that system (Torun & Tekedere, 2015).
Lopes, Miguel and Creissac (2012) also looked at usability but from the point of view of
evaluation of usability methods. They highlighted the fact that for e-learning platforms
the usability issues are related with the relationship/interactions between user and system
in the user's context (Lopes, Miguel, & Creissac, 2012). Interestingly they outlined the
logic that different e-learning platforms created for different needs should have contextual
usability features. To them, usability is based on the different designs and functionalities.
Whereas most literature focused on usability in terms of the design, pedagogy, user
interface and content, Davids et al. (2014) was incredibly interesting and shed light on the
effect of improving usability of an e- learning platform and the direct effect this has not
only adoption, but continuous use. They emphasized the fact that optimizing the usability
of e-learning materials is necessary to reduce extraneous cognitive load and maximize
their potential educational impact (Davids, Chikte, & Halperin, 2014). Their research was
based on a practical experiment or trial, this mirroed the practicality of the research by
Torun and Tekedere (2015).
17
Zaharias (2009) followed a similar theme in the literature regarding objective usability
and that is quality not only of design but content. In her work she moves a further step
and highlights usability as not standing alone, but being stronger because on intrinsic
motivation. In this case learning environments should foster intrinsically learning
motivation (Zaharias, 2009), basically the design and usability features should motivate
the e-learning students to not only take the course, but complete the courses as well. She
highlighted the fact that most e-learning platforms are occupied with usability for
adoption, but does not look at the continuity and engagement levels.
Hennessy et al. (2010) reported on developing the use of IT and communication
technology to enhance teaching and learning in East African schools. This review of
literature is incredibly diverse and sheds light on the gap of knowledge on objective
usability due to a plethora of environmental factors which affect the African schools
(Hennessy , et al., 2010). In the report factors like affordability of technology, internet
penetration, electricity and access are some of the issues which cause adoption of e-
learning technology to be lower in East Africa than the western world (Hennessy , et al.,
2010).The research though broad, does not cover objective usability of the e-learning
technology which has been implemented to date as per some of the initiatives mentioned.
This would be able to shed light on the individuals’ reactions and experiences with the
implemented technology.
Kiget, Wanyembi, and Peters (2014) focused on adoption of e-learning technology at a
Kenyan University which had adopted MOOCs to supplement physical learning. Their
study used the case study approach. As they looked at adoption they focused on the aspect
of usability. The usability attributes evaluated were user-friendliness, learnability,
technological infrastructure and policy (Kiget, Wanyembi, & Peters, 2014). Their work
was very interesting and contributed to the general literature because while adoption is
key, in their work efficient adoption is more important. Adoption is one thing but
continues use is essential, if usability of e-learning system is bad, learners fail in their
attempt to use the system (Kiget et al., 2014). They not only tested the student interaction
with the system, but the teachers loading the course content. The study concluded that
learnability (is it easy to learn to use) of e-learning systems was affecting its usability.
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2.3.1 Mobile Learning
As we narrow down into Africa, Male and Pattinson (2011) investigate socio-cultural
perspective towards quality e-learning applications. Their main research concentrates on
East Africa and African countries. They revert to the usual theme of tertiary institutions,
but with a twist for high school students. This research contributes greatly to the literature
because it touches not only on computer usability but mobile technology usability; m-
learning. The paper shows how interface design can positively enhance the quality
defining characteristics of learning in an e-learning environment (Male & Pattinson,
2011). In the research it was determined that it is essential that e-learning systems are
designed to assist learners or users achieve their societal aspirations not just emphasizing
on successful utilization of the technology, because in these cases success is measured by
the usability.
Brown (2003) presented on the role of m-learning in Africa. In this research he
highlighted that adoption of mobile technology in Africa is one of the highest in the world
ergo, the usability component is strong in m-technology and the potential for the meshing
of online learning in mobile will be efficient and effective with great potential for
adoption.“Mobile learning should prove to be a useful tool for blended training that
employs face to face remote methods” (Nyiri, 2002). Whereas this presentation did not go
deeply into the objective usability, it highlighted the potential for m-learning in Africa
and the high rate of mobile technology adoption.
2.4 The Effect of System Accessibility on Adoption of E-learning Technology
According to Batanero et al. (2014) System accessibility refers to a situation where
anyone, regardless of their personal characteristics and type of environment, is able to
access the information provided through the Learning Objects. In this case the learning
object would be e-learning technology. Similarly, Tarus, Gichoya and Muumbo (2015),
highlighted system accessibility as an essential element in the challenges which face
implementation and adoption of e-learning in Kenyan universities. In their work system
accessibility is one of the main reasons for e-learning because of the ease of access, yet
infrastructure like internet coverage, bandwidth and technology can be a hindrance to
accessibility (Tarus, Gichoya, & Muumbo, 2015).
19
Whereas most of the literature uncovered focuses on adoption of e-learning within the
formal education context, Calisir et al. (2014) focused on e-learning adoption for blue
collar employees in an automotive industry. They not only focused on perceived ease of
use and perceived usefulness, but how system accessibility affected the two and as a
result adoption of the e-learning technology. Interestingly they not only focused on
peoples’ interactions and enabling infrastructure, but the system quality as well, to them
system accessibility is one of the measures of system quality (Calisir, Gumussoy,
Bayraktaroglu, & Karaali, 2014). If all factors are held constant is the system simple to
access? This was their main premise as they interrogated system accessibility.
Park, Nam and Cha (2011) utilized system accessibility as one of the independent
variables in their model. They focused on the behavioral intention to use m-learning
technology by university students in institutions of higher learning. In their work
accessibility played a great role in enabling this form of e-learning, with SA having a
strong effect on both Perceived Ease of Use and Perceived Usefulness and ergo their
behavioral intention. The study was in Asia, but reflected on similar accessibility issues in
Africa where adoption of mobile technology in Africa is one of the highest in the world
ergo (Brown, 2003). Mobile learning should prove to be a useful tool for blended training
that employs face to face remote methods (Nyiri, 2002); mobile phones make
accessibility to e-learning technology more efficient, especially in a market with positive
mobile access.
According to Masud and Huang (2012) access plays an important role in the adoption of
e-learning technology, with a focus on cloud computing. According to this work, cloud
computing allows quicker and more consistent access to e-learning technology. This work
was a new twist on e-learning adaptation as other researchers focused on the systems and
internet access more than the cloud storage factors. Infrastructure like internet coverage,
bandwidth and technology can be a hindrance to accessibility (Tarus et al., 2015) as such
Masud and Huang (2012) also discuss government policies and essential infrastructure.
Debevc et al. (2007) shed an interesting light on the work surrounding accessibility. They
focused on a case study of a project applied in Slovenia for those with hard of hearing and
deaf people and the work was a report reflecting results of the e- learning project. In their
work e- learning was utilized to create more learning accessibility, but at the same time
the e-learning systems we designed to necessitate ease of access and impact. In their
20
work, accessibility was not just about the technical aspect of the platform, but the delivery
of the content (Debevc et al., 2007); this highlighted an interesting angle to assessing
accessibility. It is more than just accessing the system but once accessed, can they access
the content?
Lee, Hsiao and Purnomo (2014) work was very interesting in that they looked at
accessibility as one of the system characteristics which are essential to adoption of e-
learning technology in Indonesian universities. Whereas Debevc et al. (2007) looked at
accessibility in regard to the content and technological design, Lee, Hsiao and Purnomo
(2014) looked at accessibility from an infrastructure point of view observed that students
in Indonesian universities continue to encounter connectivity problems similar to other
developing countries. Their study suggested system characteristics that support e-learning
activities (learning content and technology accessibility) are critical in increasing
students’ behavioral intention to adopt e-learning systems (Lee, Hsiao, & Purnomo,
2014).
In their work ‘towards the design of personalized accessible e-learning environments’
Brahim et al., (2013) discussed system accessibility and the importance in adoption of e-
learning technology. Their paper highlighted their vision in designing an environment
supporting eLearning accessibility. In order to ensure offering content in formats tailored
to individual users based on their personal preferences. Their study reflected results
similar to Debevc et al., (2007) where system accessibility is not only based on
infrastructure but content as well. In their case their research highlighted the factor of
personalization of e-learning experience creating more ease of access for different
audiences or individuals. They concluded that e-learning accessibility ensures that
resources can be used by all learners regardless of environmental or technological
constraints, and allows individual learning styles and preferences to be accommodated
(Brahim et al., 2013).
Batanero et al. (2014) contributed greatly to this literature due to their approach to
accessibility research and its effect on adoption of technology. In their study they looked
at the relationship between accessible learning objects and accessible web content, they
did this by contrasting and comparing to certain ISO standards for each of the variables.
Similar to the study by Debevc et al. (2007) they reflected on learning objects having
visual, audio and text variations adapted to different student needs including those with
21
disability. They went a step beyond that and specified that learning objects need
metadata to allow students to return to the specified requirement they needed in the first
place. In their work their recommendations focus on web content, but also on user
interface and navigation, which now leans toward objective usability. They highlighted
three accessibility levels of A, AA and AAA (Batanero et al., 2014). These levels
progressed from making sure people can access the web systems, to making sure they do
not have serious problems accessing the systems and finally ensuring they do not have
some issues accessing the system. All this needs to be taken into consideration by the
developers of the platform. Batanero et al. (2014) concluded that there needs to be
balance between the standards of the learning objectives as well as the web content to
allow for optimum accessibility for all students.
Cooper, Colwell and Jelfs (2007) contributed greatly to the literature due to the clear
linkage between usability and accessibility. Former literature only alluded to this, but
from the beginning they declared that accessibility and usability are intrinsically linked
(Cooper, Colwell, & Jelfs, 2007). They highlighted the need to constantly evaluate
accessibility and usability in order to ensure ease of use and adoptability of the e-learning
resources. In their research they concluded that Accessibility and usability are
intrinsically linked. The lower the level of accessibility of a resource for an individual, the
less usable it will be for them. In the worst case they will not be able to use it at all.
Conversely, improved accessibility for disabled users promotes usability for all (Cooper,
Colwell, & Jelfs, 2007).
Varonis (2015) focused on key legislation and cases in which accessibility for those with
disabilities was not adequate and the resulting effects, as well as the importance of design
in course development which will allow for accessibility without having to make
exceptions because all the basis were covered in the first place. She concluded that given
the challenges of creating accessible content that provides equivalent information to all
learners, faculty and course designers can implement the principles of Universal Design
to enhance the learning environment for all students and ensure they are in compliance
with guidelines and regulations (Varonis, 2015). This compliance is facilitated by
emerging standards for accessible content and emerging technologies for making content
accessible to all without the need for special accommodations. Her work reflected that of
Debevc et al. (2007) whose work reflected on learning objects having visual, audio and
text variations adapted to different student needs including those with disability.
22
Alkhattabi, Neagu and Cullen (2010) approached accessibility form the angle of quality.
Their work focused on an information quality framework for e-learning systems. They
proposed 14 information quality attributes grouped in three quality dimensions: intrinsic,
contextual representation and accessibility. Similar to the work of Batanero et al. (2014),
their focus on accessibility is not just on system design, but delves into the content and
the effect on accessibility. In their framework accessibility is mainly described as
‘accessibility data quality’ and refers to the quality aspects concerned into accessing
distributed information (Alkhattabi, Neagu, & Cullen, 2010). There were 4 dimensions to
guaranteeing accessibility; access security, availability, response time and accessibility
itself. This contributed greatly to the literature, due to the clarity of the proposed model
and the importance given to accessibility.
Similarly, Ramayah, Ahmad and Chiun-lo (2010) focused on the role of quality factors in
not only adoption, but continuous use e-learning systems in Malaysian Universities. In
this work, system accessibility played an essential role in the quality of an e-learning
technology. They stated that despite the popularity of the Internet, many people resist
using it due to the slow response time, caused by poor design of the web sites or simply
heavy traffic on the Internet, and lack of system accessibility (Ramayah, Ahmad, &
Chiun-Lo, 2010). They concluded that reliability and accessibility of e-learning system
thus, can be said to have a certain influence in the usage of it.
Tarus, Gichoya and Muumbo (2015) as well as most authors focus on system accessibility
as the infrastructure, internet, computer and laptops, while Calisir et al (2014) focus on
the e-learning system quality. Whereas this is all well and good the aspect of mobile
learning is essential with Africa at the fore front of mobile use, not everyone has a
computer but most have a mobile phone. Mobile technologies have the power to make
learning even more widely available and accessible than we are used to in existing e-
learning (Brown, 2003). This makes one ask the question of whether the e-learning
platforms are easily accessible on mobile.
According to Urhiewhu and Emjoroh (2015), one of the major factors which affect the
adoption of technology for digital information resources is lack of system access.
Basically there are an inadequate number of computers to access digital information
sources. In this study of university students from Edo Nigeria, the system accessibility
was in the form of resources provided by the universities to enable students to access the
23
digital resources effectively. Yet this is quite the gap. This contributes to the literature,
iterating access as one of the factors which can affect the adoption of technology for
forms of e-learning.
Njenga and Fourie (2010), shed light on a varied way to think of system accessibility. In
this work accessibility is mainly in reference to internet access with which the e-learning
platforms can be accessed. As he highlights this challenge in the adoption of e-learning
technology he also highlights a critical factor which ruffles some feathers, ‘accessible
information does not turn automatically into meaningful knowledge without the
assistance of a teacher or an expert (Njenga & Fourie, 2010). Once again this context is
skewed to formal higher education.
As Brown (2003) presented on the role of m-learning in Africa. His research highlighted
that adoption of mobile technology in Africa is one of the highest in the world and as
such system accessibility is strong in m-technology and the potential for adoption by a
large number of Africans is an upcoming reality. He based his arguments on the evolution
of social media networks and the accessibility of mobile devices. He concluded that
mobile learning should prove to be a useful tool for blended training that employs face to
face remote methods.
2.5 Chapter Summary
This literature review section aided the understanding of the three variables of computer
self-efficacy, objective usability and system accessibility and how the interrelated
relationships between the three affect Adoption of e-learning technology. The technology
acceptance model played a great role in forming the basis of the arguments which were
then tested and measured in different environments and situations around the world. The
literature from African cases was not as prominent as that of European and Asian cases,
which solidified the basis of this thesis- to provide and aid in filling the gap for relevant
measures and literature for Africa.
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CHAPTER THREE
3.0 RESEARCH METHODOLOGY
3.1 Introduction
The purpose of this study was to investigate factors affecting adoption of e- learning
technology in Kenya. This chapter describes the research design, target population,
sampling design, data collection methods, research procedures and data analysis methods
that were used in this study.
3.2 Research Design
According to Krishnswami and Satyaprasad (2010) research design is a logical and step
by step plan prepared for directing a research study. It specifies the objectives of the
methodology and the techniques to be utilized for achieving the objectives set out in the
study.Sreejesh, Mohapatra and Anusree (2014) noted that there are three major types of
research designs, such as exploratory, descriptive and causal research designs.
Exploratory design is one in which little is known about the topic, in this case a lot of
preliminary experiments need to occur. A descriptive study is undertaken in order to
ascertain and be able to describe the characteristics of the variables of interest in a
situation (Sekran, 2003). Explanatory studies on the other hand go beyond descriptive
observation, this type of research focuses on the cause and effect relationship between
variables. (Sreejesh, Mohapatra, & Anusree, 2014).
This study used explanatory research design to investigate the factors which affect
adoption of e-learning technology in Kenya. Studies that engage in hypotheses testing
usually explain the nature of certain relationships, or establish the differences among
groups or the independence of two or more factors in a situation (Sekran, 2003). An
explanatory research design is fitting for this study because it will help us ascertain not
only the relationship between the different variables, but measure the effect and strength
of each independent variable on the dependent variable which in this case is technology
adoption.
3.3 Target Population
Target population is the aggregate of elements about which we wish to make inferences
(Satyaprasad & Krishnaswami, 2010).According to Cooper and Schindler (2014) a target
population is those people, events, or records that contain the desired information for the
25
study that determine whether a sample or a census should be selected. Basically, this is
the population from which the sample is taken. This study focused on social media
(Facebook) users in Kenya. According to Internet World Statistics there are over 6.2
million Facebook subscribers in Kenya as of June 2017 (Internet world Stats, 2017).
3.4 Sampling Design
The sampling design is comprised of several variables; the sampling frame, sampling
technique and finally sampling size. According to Cooper and Schindler (2014) the
sampling design is the method and process used to form a specific population and
therefore it is the procedure that a researcher goes through while selecting items for the
study sample.
3.4.1 Sampling Frame
Sreejesh et al. (2014) define the sample frame as the list of population elements or
members (individuals or entities) from which units to be sampled are selected. This study
focused on social media users in Nairobi who are interested in e-learning or have ever
learned online.
3.4.2 Sampling Technique
A sampling technique is the name or other identification of the specific process by which
the entities of the sample have been selected. Basically, the way in which a sample is
selected. There are a variety of sampling techniques are available and they may be
classified by their representation basis and element selection techniques (Cooper &
Schindler, 2014). A sample can be selected in two ways from a population-through
probability sampling, or through non-probability sampling (Sreejesh, Mohapatra, &
Anusree, 2014).
According to Sekaran (2003), in probability sampling, the elements in the population
have some known chance or probability of being selected as sample subjects. In non-
probability sampling, the elements do not have a known or predetermined chance of being
selected as subjects. According to Sreejesh et al. (2014) a sample that is selected using
probability sampling techniques will be sufficient for getting effective results.
This study utilized the geographic cluster, simple random sampling technique with a
focus on Nairobi. According to Cooper and Schindler (2014) the simple random sample is
26
considered a special case in which each population element has a known and equal
chance of selection. This worked for the sample due to the unrestricted nature.
Table 3.1: Clusters
Facebook Group Follower Count
E-Limu 3,800
Zydii 4,700
Edtech Nairobi 1,300
Personal Page 791
Total Population 10,591
3.4.3 Sample Size
The sample size is an essential aspect of any empirical study in which the goal is to make
deductions about a population from a sample. According to Sekaram (2014) the need for
choosing the right sample for a research investigation cannot be overemphasized. We
know that rarely will the sample be the exact replica of the population from which it is
drawn, which is why it is essential to get correct. Cooper and Schindler (2014) note that
cost and resources also need to be considered in the determination of sample size. The
study focused on clusters of Kenyan Facebook groups with an interest or focus on e-
learning. This study adopted the formula adopted by Cochran (2014) to determine the
sample size;
Where: e is the desired level of precision (i.e. the margin of error), p is the (estimated)
proportion of the population which has the attribute in question, and q is 1 – p. With the
assumption of 85% of the population have interacted with an e-learning platform, so p =
0.85. 95% confidence, (gives us Z values of 1.96) and at least 5 percent—plus or minus—
precision. The q value is 1-0.85=0.15
((1.96)2 (0.85) (0.15)) / (0.05)2 = 196.
27
Total sample size is 196.
3.5 Data Collection Methods
This study used a questionnaire to collect data from social media users in Nairobi who
have an interest in e-learning. According to Rowley (2014) questionnaires refer to
documents that include a series of open and closed questions to which the respondent is
invited to provide answers. The questionnaire was divided into several sections and aimed
to first capture the demographic information, followed by the questions focusing on the
variables and research objectives. The questionnaires were self-administered online. The
responses were through a 5 level Likert scale with a range from 1 to 5 where; 1- strongly
agree, 2-agree, 3-neutral,4 disagree, 5-strongly disagree.
3.6 Research Procedure
According to Cooper and Schindler (2014) the research procedures used should be
described in sufficient detail to permit another researcher to repeat the research. This
includes the permissions, pilot study (if any), reliability of instruments, validity of the
instruments, administration of the elements and ethical considerations.
Permission to conduct the research was granted in stages. The first stage is permission
granted from the research supervisor, followed by the Dean, Chandaria School of
Business.
According to cooper and Schindler (2014) Reliability is concerned with estimates of the
degree to which a measurement is free of unplanned or unstable error. Reliable
instruments can be used with confidence that temporary and situational factors are not
interfering. Basically, how consistent is the tool? This is essential for replicability and
sustainability of research. There are two types of reliability; internal and external
reliability. Internal consistency of data can be established when the data give the same
results even after some manipulation (Sreejesh, Mohapatra, & Anusree, 2014). There
needs to be homogeneity in all the factors and elements (Sekran, 2003). External
reliability simply focuses on replication of the study.
One of the best measures of reliability is Cochran’s alfa. According to Sekran (2014)
Cronbach’s alpha is a reliability coefficient that indicates how well the items in a set are
positively correlated to one another. Cronbach‘s alpha is computed in terms of the
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average inter-correlations among the items measuring the concept (Tavakol & Dennick,
2011). The closer Cronbach‘s alpha is to 1, the higher the internal consistency reliability.
According to Cooper and Schindler (2014) even when an experiment is the ideal research
design, it is not without problems. There is always a question about whether the results
are true. This is where validity comes in, how true are the results after use of the tool?
Validity can be measured through several methods like face validity- unified agreement
by expert on validity of tool, content validity- relevant variables for measurement, and
construct validity- degree of measurement instrument to logically connect the
fundamental theory. Criterion-related validity is the degree to which a measurement
instrument can analyze a variable that is said to have a criterion (Sreejesh, Mohapatra, &
Anusree, 2014). A good example would be if you can measure the length of a room using
a tape measure, you should also be able to do so using a ruler and string. The tool
developed by Davies (1989) has been the basis of research for perceived ease of use and
technology adoption for the last 28 years. It is simply adapted to fit different scenarios,
but it is replicable.
According to Rowley (2014) one of the main advantages of questionnaires is the ability to
make contact with and gather responses from a relatively large number of people in
dispersed and possibly remote locations. The questionnaires for this study were self-
administered. In self-administered studies, the interviewer’s main role is to inspire
participation as the participant completes the questionnaire on his or her own(Cooper &
Schindler, 2014).According to Sekran (2014) modern technology is now playing a major
role in data collection, giving more flexibility. Questionnaires have the advantage of
gaining data more efficiently in terms of researcher time, resources, energy, and costs.
This study utilized an e-questionnaire created on Google forms and was distributed
different channels of social media including Facebook.
Ethics in research has to do with the manner in which the researcher approaches the
research, basically their behavior in regards to the rights of their respondents. (Sekran,
2014; Cooper & Schindler, 2014). According to Cooper and Schindler (2014), the
objective of ethics in research is to ensure that no one is harmed or suffers adverse
consequences from research activities. Ethical behavior permeates each step of the
research process- data collection, data analysis, reporting, and dissemination of
information on the Internet, if such an activity is undertaken (Sekran, 2003). Research
29
misconduct includes ‘fabrication, falsification or plagiarism in the process of carrying out
the research. This research was done without any pressure from the researcher;
respondents chose whether to fill the questionnaires’ and the rate at which they completed
was solely based on their time and willingness.
3.7 Data Analysis Methods
Data analysis involves data preparation, descriptive statistics and inferential statistics.
According to Cooper and Schindler (2014) Data analysis is the methods used to analyze
the data and describes data handling, preliminary analysis, statistical tests, computer
programs, and other technical information.
There are several stages of data preparation. If there are blank responses they have to be
handled in some way, the data coded, and a categorization scheme has to be set up. The
data will then have to be imputed, and some software program used to analyze them
(Sekran, 2003). According to Cooper and Schindler (2014) it is during this step that data
entry errors may be revealed and corrected. I went through the process of data coding and
validation, handling of the blank responses as well as non-varied responses.
Descriptive statistics are measurements which illustrate the center, spread and shape of
distributions and are helpful as the first stage for the description of prepared data. They
help to summarize the data in a simple and visual way (Cooper & Schindler, 2014).
According to Sekran (2014) descriptive statistics such as maximum, minimum, means,
standard deviations, and variance were obtained for the interval-scaled independent and
dependent variables. Basically they help us with the measures of central tendency. This
sort of analysis may describe data on one variable, two variables or more than two
variables. Accordingly it is called univariate analysis, bivariate analysis and multivariate
analysis respectively (Krishnaswami & Satyaprasad, 2010).
According to Cooper and Schindler (2014) includes the estimation of population values
and the testing of statistical hypotheses. In inferential statistics we might be interested to
know or infer from the data through analysis (1) the relationship between two variables
(e.g., between advertisement and sales), (2) differences in a variable among different
subgroups (Sekran, 2003). There are several inferential tests including; correlation,
analysis of variance and regression. In this research I utilized structural equation model,
which is a multivariate statistical analysis method, this is due to the latent factors in the
technology acceptance model (Davies, 1989).
30
3.8 Chapter Summary
The chapter managed to clearly describe the methodology that the study used to reach the
objective of the study. The research methodology was expounded in sections which
include; research design, target population, sampling design, sampling frame, sampling
technique, sample size, data collection, research procedure, and data analysis methods.
Chapter four will discuss data analysis and presentation of study findings.
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CHAPTER FOUR
4.0 RESULTS AND FINDINGS
4.1 Introduction
The purpose of this study was to investigate the factors affecting the adoption of e-
learning technology in Kenya. This chapter presents the data analysis results,
interpretation and presentation, and the findings from the research study that were
analyzed using the SPSS version 20 and SPSS AMOS version 25.
4.2 Response Rate
The study focused on the factors affecting the adoption of e- learning technology in
Kenya. A total of 196 respondents were expected to participate in the study electronically
by use of Google questionnaire. The researcher managed to get a response rate of 186 by
the time of closure of study. This gave a response rate of 95%. The response rate helps to
produce accurate useful results that represent the target population.
Table 4.1: Response Rate
Questionnaires Frequency Percentage
Responded 186 95
Did not respond 10 5
Total 196 196
4.3 Demographic Characteristics
This section discusses the results of the general information about the respondents. This
includes the gender, age bracket, marital status, education background, and if the
respondents have ever learnt something online.
4.3.1 Gender of Respondents
Figure 4.1 presents the gender of the respondents; 55% of the respondents were female
and 45% of the respondents were male. The findings indicate female who participated in
the study were slightly more than male.
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Figure 4.1: Gender
4.3.2 Age Bracket
Figure 4.2 indicates the age bracket of the respondents; 58% of the respondents who
constituted the majority were in the age bracket of 26-32 years. They were followed by
those who were in the age bracket of 18-25 years at 22%, 33-40 years were 16% while
lastly, 4% were above 40 years.
18-2522%
26-32
58%
33-40
16%
above 40
4%
Figure 4.2:Age Bracket
33
4.3.3. Level of Education
The respondents were asked to indicate their level of Education. As shown on figure 4.3,
59% were undergraduate while the remaining 41% were postgraduates.
Figure 4.3: Level of Education
4.3.4 Marital Status
The respondents were asked to indicate their marital status. Majority indicated they were
single (74%) and the remaining 26% indicated they were married. Figure 4.4 shows this.
Figure 4.4: Marital Status
34
4.3.5 Online Learning
The study focused on adoption of online learning hence only respondents from those who
were online was significant. When asked to state if they had learnt something online, 99%
stated they had while only 1% stated s/he had not as indicated on figure 4.5.
Yes99%
No
1%
Figure 4.5: Online Earning
4.4 Descriptive Analysis of Study Variables
4.4.1 Independent Variables
The independent variable of study was clustered into three sectors based on the research
questions; self-efficacy (SE), objective usability (OU) and system accessibility (SA). The
presentation of the descriptive shows all the variables were highly rated as agreed and
strongly agreed as indicated on table 4.2.
On SE, ‘I feel confident finding information on e-learning (online learning) systems’ was
highly rated as strongly agreed at 47.1% and agreed at 39.3%. The second question ‘I
have the necessary skills for using an e-learning (online learning) system’ was highly
rated as strongly agreed at 44.3% and agreed at 40.7%.
Response on OU was also similar as follow ‘Once I use an e-learning system, I can easily
remember how to navigate it’ was highly rated as strongly agreed at 36.4% and agreed at
46.4% and ‘E-learning (online learning) systems save me time’ was highly rated as
35
strongly agreed at 34.3% and agreed at 47.1%. However, the question on ‘Most e-
learning (online learning) systems are easy to use’ was rated differently with agreed at
45.0% and neutral at 26.4%.
On SA, the response were; ‘I can access e-learning (online learning) systems on my
mobile phone’ was highly rated as strongly agreed at 29.8% and agreed at 38.3%. While
the question on ‘I have no difficulty accessing and using an e-learning (online learning)
systems in Kenya’ was ranked differently with agreed at 39.4% and neutral at 26.6%.
Table 4.2:Descriptive of Independent Variables
SD D N A SA
SE1 I feel confident finding information on e-
learning(online learning )systems
3.6 1.4 8.6 39.3 47.1
SE2 I have the necessary skills for using an e-
learning (online learning) system
2.9 1.4 10.7 40.7 44.3
OU1 Most e-learning (online learning) systems are
easy to use
4.3 10.7 26.4 45.0 13.6
OU2 Once I use an e-learning system, I can easily
remember how to navigate it.
2.9 2.1 12.1 46.4 36.4
OU3 E-learning (online learning) systems save me
time.
4.3 3.6 10.7 47.1 34.3
SA1 I have no difficulty accessing and using an e-
learning (online learning) systems in Kenya.
3.7 13.3 26.6 39.4 17.0
SA2 I can access e-learning (online learning)
systems on my mobile phone
4.3 9.6 18.1 38.3 29.8
4.4.2 Latent Variables
The study had two latent variable of study which were treated as intervening variables.
The two variables were attitude (ATT) and behavioral intention (BI) to indulge in e-
learning. The response on attitude were positive as follow: ‘Studying through e-learning
is a good idea’ rated highly as agree at 41.4% and strongly agreed at 30.0%. ‘Studying
36
through e-learning is a sensible idea’ was also rated highly as agreed at 43.6% and
strongly agreed at 33.6%. Lastly ‘I have positive thoughts toward e-learning’ was rated
highly as agreed at 47.9% and strongly agreed at 37.1%. Questions on BI response were:
‘I intend to check announcements from e-learning (online learning) systems frequently’
was rated highly as neutral at 34.3% and agreed at 29.3%. Similarly, the lastly question
‘Intend use e-learning (online learning) systems quite a bit’ was rated highly as agreed
38.6% and neutral at 27.9%. Table 4.3 presents the output.
Table 4.3: Descriptive of Latent Variables
SD D N A SA
ATT1 Studying through e-learning is a good idea. .7 5.0 22.9 41.4 30.0
ATT2 Studying through e-learning is a sensible idea .7 .7 21.4 43.6 33.6
ATT3 I have positive thoughts toward e-learning 1.4 2.1 11.4 47.9 37.1
BI1 I intend to check announcements from e-
learning (online learning) systems frequently.
8.6 13.6 34.3 29.3 14.3
BI2 Intend use e-learning (online learning)
systems quite a bit
5.0 7.1 27.9 38.6 21.4
4.4.3 Dependent Variables
The descriptive of the dependent variables were presented on table 4.4 as follow.
Question on Perceived ease of use (PEOU) were ‘I find e-learning (online learning)
systems easy to use’ which was highly rated as agreed at 53.6% and strongly agreed at
20.7%. ‘Learning how to use an e-learning system is easy for me’ was highly rated as
agreed at 51.4% and strongly agreed at 27.1%. Lastly ‘It is easy to become an expert at
using an e-learning (online learning) system’ was highly rated as agreed at 39.3% and
neutral at 27.1%.
There were three questions on Perceived usefulness (PU), ‘E-learning (online learning)
would improve my learning experience’ was highly rated as agreed at 42.1% and strongly
agreed at 36.4%. ‘I can us e-learning (online learning) to increase my personal and
professional skills’ was highly rated as agreed at 40.7% and strongly agreed at 50.7% and
37
lastly ‘E-learning could make it easier to study course content (instead of physical
classes)’ was highly rated as strongly agreed at 32.1% and neutral at 27.1%.
Table 4.4: Descriptive of Dependent Variables
SD D N A SA
PEU1
I find e-learning (online learning)
systems easy to use.
3.6 5.0 17.1 53.6 20.7
PEU2
Learning how to use an e-learning
system is easy for me.
2.1 4.3 15.0 51.4 27.1
PEU3
It is easy to become an expert at using
an e-learning (online learning) system.
.7 8.6 27.1 39.3 24.3
PU1
E-learning (online learning) would
improve my learning experience
1.4 2.9 17.1 42.1 36.4
PU2
I can us e-learning (online learning) to
increase my personal and professional
skills.
.7 .7 7.1 40.7 50.7
PU3
E-learning could make it easier to
study course content (instead of
physical classes)
1.4 12.9 27.1 26.4 32.1
4.5 Inferential Analysis
The inferential analysis conducted was in three folds. The first covers the statistical tests
required to identify which model bests fits the data. The second covers the factor analysis
and last part covers the Structure equation model (SEM) that answered the hypothesis of
study.
4.5.1 Normality Test
Skewness and kurtosis statistics were used to test the normality of the items of the
variables and results were shown in table 4.5. Skewness and kurtosis statistics in the
range -2.0 and + 2.0 imply satisfaction of normality. All the items in the tool followed a
normal distribution.
38
Table 4.5: Normality Test Using Skewness and Kurtosis Statistics
Skewness Kurtosis
SE1 -1.689 3.338
SE2 -1.514 2.903
OU1 -.650 .111
OU2 -1.347 2.432
OU3 -1.373 2.006
SA1 -.577 -.170
PEU1 -1.090 1.470
PEU2 -1.065 1.570
PEU3 -.393 -.444
PU1 -.953 1.038
PU2 -1.360 3.046
PU3 -.373 -.886
ATT1 -.579 -.054
ATT2 -.584 .310
ATT3 -1.196 2.264
BI1 -.310 -.494
BI2 -.665 .133
4.6 Measurement Model
The hypothesized relationship was estimated using structural equation model (SEM). The
first stage explores the data through exploratory factor analysis (EFA). The second stage
computes the confirmatory factor analysis (CFA) that estimates the measurement model
on multiple criteria such as internal reliability, convergent, and discriminant validity. The
analysis were done using AMOS version 25.
4.6.1 Exploratory Factor Analysis
Exploratory factor analysis was used to refine the variables in the study. It covers the
factor loading matrix, communalities and total variance extracted by principal
39
components analysis (PCA) method. The KMO measure of Sampling Adequacy measure
was .871 which shows the sample was adequate for factor (values closer to 1 are better).
Bartlett’s test of Sphericity show a Chi-Square of 1821.883 with associated significant P-
value of 0.000<0.05. this shows the items were statistically significant in measuring SE,
SA, OU, BI, ATT, PU and PEU. The Kaiser Meyer-Olin Measure of Sampling Adequacy,
Bartlett’s Test of Sphericity and communalities tests shows the data collected was good
for factorability as indicated on table 4.6.
Table 4.6: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .871
Bartlett's Test of Sphericity
Approx. Chi-Square 1821.883
Df 153
Sig. .000
4.6.2 Total Variance Explained
Table 4.7 indicates six factors were developed from the variance with the Eigen values
greater than .8 and presents 74.1% of the cumulative samples of square loading. The four
factors were pulled out based on kaiser’s criterion. They were further expounded on the
pattern matrix.
40
Table 4.7: Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared
Loadings
Rotation
Sums of
Squared
Loadingsa
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
%
Total
1 7.559 41.996 41.996 7.559 41.996 41.996 5.215
2 1.702 9.455 51.450 1.702 9.455 51.450 4.649
3 1.315 7.305 58.755 1.315 7.305 58.755 5.297
4 .965 5.360 64.114 .965 5.360 64.114 4.475
5 .940 5.225 69.339 .940 5.225 69.339 4.349
6 .855 4.748 74.087 .855 4.748 74.087 3.712
7 .703 3.903 77.990
8 .625 3.472 81.463
9 .544 3.020 84.483
10 .506 2.811 87.294
11 .443 2.460 89.753
12 .400 2.224 91.977
13 .329 1.828 93.805
14 .306 1.699 95.504
15 .266 1.478 96.982
16 .225 1.251 98.233
17 .195 1.084 99.317
18 .123 .683 100.000
Extraction Method: Principal Component Analysis.
a. When components are correlated, sums of squared loadings cannot be added to obtain
a total variance.
41
4.6.3 Pattern Matrix
Communality measures the percent of variance in a specified variable explained by all the
combined factors and is interpreted as the reliability of the indicator. A low value for
communality (less than 0.32) shows that the specific variable does not fit well with other
variables hence extracted. In this study, all the factors had a higher value of greater than
.6 hence indicating they were strong and fit with other variables. From the pattern matrix,
all the six variable of study were extracted as indicated on table 4.8. The factors were;
ATT, SE, OU, PEU, PU, and BI. All the factor loadings were greater than 0.5, an
indication that the measures were well loaded. (See table 4.8)
42
Table 4.8: Communalities and Pattern Matrix
ATT SE OU PEU PU BI Communal
ity
SE1 .939 .772
SE2 .958 .808
OU1 .598 .631
OU2 .722 .709
OU3 .737 .712
SA1 .596 .540
SA2 .875 .642
PEU1 .564 .782
PEU2 .767
PEU3 .916 .754
PU1 .635 .691
PU2 .613 .684
PU3 .994 .766
ATT1 .941 .810
ATT2 .891 .842
ATT3 .771 .803
BI1 .881 .821
BI2 .830 .801
Extraction Method: Principal Component Analysis.
Rotation Method: Promax with Kaiser Normalization.
a. Rotation converged in 8 iterations.
4.6.4 Confirmatory Factor Analysis.
Confirmatory factor analysis (CFA) was done to measure the reliability and validity of
the item measurements developed from the EFA. This was done using AMOS version 25
to measure the model fitness. The CFA model is shown in figure 4.6;
43
Figure 4.6: Confirmatory Factor Analysis Model for Study Variable
44
4.6.5 Model fits for CFA Model
Table 4.9 presents the model fit measurement statistics for the overall measurement
model for study variables. The fit statistics indices were within the satisfactory range
therefore the CFA model fit the data adequately.
Table 4.9: Model Fits for CFA Model
Measure CMIN DF CMIN/DF GFI CFI RMSEA PCLOSE
Estimate 145.871 89 1.639 0.914 0.959 0.058 0.201
Threshold -- -- Between
1 and 3
>0.90 >0.90 <0.08 >0.05
Interpretation -- -- Excellent Excellent Excellent Excellent Excellent
4.6.6 Construct Reliability
Construct reliability was assessed using the Cronbach’s alpha reliability and variance on
the estimates. The variance of all the estimates were all less than .5 hence minimal while
the Cronbach’s alphas values were all above the 0.7 indicating that all the variables in the
study were reliable as indicated in table 4.10.
Table 4. 10: Construct Reliability
Estimates SE Cronbach’s alphas Item removed
PEU .358 .088 0.726 PEU2
PU .406 .099 0.741 None
SE .538 .094 0.823 None
BI .723 .126 0.765 None
OU .349 .096 0.714 OU2
ATT .480 .065 0.885 None
45
4.6.7 Convergent Validity.
To evaluate convergent validity, the inter-item correlation matrix was used as indicated
on table 4.11. In all the values, the matrix was more than .32 and less than .90 indicating
the measurement scales revealed satisfactory measurement validity.
Table 4.11: Inter-Item Correlation Matrix
PEU PU SE BI OU ATT
PEU 1.000 .422 .441 .465 .598 .503
PU .422 1.000 .337 .452 .456 .608
SE .441 .337 1.000 .259 .374 .383
BI .465 .452 .259 1.000 .465 .490
OU .598 .456 .374 .465 1.000 .540
ATT .503 .608 .383 .490 .540 1.000
4.6.9 Correlation Coefficient.
Table 4.12 indicates the correlation coefficients. PEOU and PU were positively correlated
with other independent variables. PUOE correlation results were; with SE (r=0.441,
p<0.05), with BI (r=0.465, p<0.05), with OU (r=0.598, p<0.05), and with ATT (r=0.503,
p<0.05).
For PU, the correlation response were; with SE (r=0.337, p<0.05), with BI (r=0.452,
p<0.05), with OU (r=0.456, p<0.05) and lastly with ATT (r=0.514, p<0.05). This further
shows the strength of the correlation is high for PEOU with the independent variables
than PU with the independent variables as indicated on table 4.12.
46
Table 4.12:Correlation Coefficient
PEU PU SE BI OU ATT
PEU 1
PU .422** 1
SE .441** .337** 1
BI .465** .452** .259** 1
OU .598** .456** .374** .465** 1 .540**
ATT .503** .608** .383** .490** .540** 1
**. Correlation is significant at the 0.01 level (2-tailed).
4.7 Structural Estimation Model (SEM)
Figure 4.7: Structural Model for the Relationship of the Study Variables
47
4.7.1 Model Fits for Structural Model
The model fit was determined by CMIN/DF, GFI, CFI, RMSEA and PCLOSE. As
indicated on table 4.13, the model was weak for perdition of the effect between the
independent, latent and dependent variables. The model result was not within the required
range of measure on GFI, RMSEA and PCLOSE hence the model was weak.
Table 4.13: Model Fits for Structural Model
Measure CMIN DF CMIN/DF GFI CFI RMSEA PCLOSE
Estimate 183.210 92 1.991 0.897 0.935 0.073 0.09
Threshold -- -- Between
1 and 3
>0.90 >0.90 <0.08 >0.05
Interpretation -- -- Excellent Good Excellent Good Poor
To further understand the model, Standardized Residual Covariances (Group number 1 -
Default model) showed a significant difference on measure of variables. For a good
model, the standardized residual covariance measure is normally distributed; with
standard deviation (SD) between -2 to 2 absolute values. As indicated on table 4.14
extracted from standardized residual covariance table, the SD of SE1 and SE2 values
were higher than -2 or +2 hence excluded from the model. Find on appendix II the full
table.
48
Table 4.14: Standardized Residual Covariances (Group number 1 - Default model)
ATT1 ATT2 ATT3 OU1 OU3 SA2 BI1 BI2 SE1 SE2
ATT1 0.515
ATT2 0.891 0.636
ATT3 0.554 0.649 0.629
OU1 -0.359 0.195 0.378 0
OU3 0.631 1.034 1.488 0.053 0
SA2 -0.506 -1.029 0.006 0.34 0.426 0
BI1 1.221 -0.156 1.492 0.31 -0.327 1.671 0.315
BI2 2.259 1.364 2.736 -0.037 -0.027 -0.101 0.4 0.349
SE1 2.768 2.316 2.155 5.297 3.225 2.341 1.276 2.642 0
SE2 2.168 3.009 2.193 5.41 4.4 2.281 1.012 1.551 0.004 0
PU1 0.939 0.874 0.906 1.286 1.177 -0.203 0.691 1.086 2.213 0.957
Following the extraction of SE1 and SE2 due to high SD, the developed model was as
indicated on the next page.
49
4.7.2 Improved Model
Figure 4.8 : Improved Structural Model for the Relationship of the Study Variables
4.7.3 Model Fits for Structural Model
Table 4.15 presents the model fit measurement statistics for the overall structural model
for study variables. The fit statistics indices were within the satisfactory range therefore
the structural model fit the data adequately hence the improved model was fit for the
study.
50
Table 4.15: Model fits for Structural Model
Measure CMIN DF CMIN/DF GFI CFI RMSEA PCLOSE
Estimate 96.348 69 1.395 0.932 0.977 0.046 0.603
Threshold -- -- Between
1 and 3
>0.90 >0.90 <0.08 >0.05
Interpretation -- -- Excellent Excellent Excellent Excellent Excellent
4.8 Regression Weights
4.8.1 Effect of Computer Self-Efficacy on Adoption of E-learning Technology
The path coefficient for the relationship between SE and adoption of e-learning was weak
and not significant hence dropped from the model. As indicated on figure 4.7, SE was
weak due to high SD and was dropped from the model.
4.8.2 Effect of Objective Usability on Adoption of E-Learning Technology
The path coefficient for the relationship between OU and adoption of e-learning was
significant in three levels as shown on table 4.16 regression weight and figure 4.8.
4.8.2.1 Without Latent/Intervening Variable
The path coefficient for the relationship between OU and PEOU was positive and
significant at the 0.05 level (βeta=0.938, T-value =3.658, p<0.05) as indicated on table
4.16 and figure 4.8. The positive relationship indicates that one unit increase in OU will
result in 0.938 increase in PEOU. Similarly, relationship between OU and PU was
positive and significant at the 0.05 level (βeta=0.26, T-value =3.562, p<0.05) as indicated
on table 4.16 and figure 4.8. The positive relationship indicates that one unit increase in
OU will result in 0.26 increase in PU. Hence OU affects adoption of e-learning
technology.
4.8.2.2 With BI as Latent/Intervening Variable.
The path from OU to PEOU or to PU though BI as intervening variable was not
statistically significant (p> .05) hence concludes BI as latent/intervening variable does
not influence OU to adoption of e-learning technology.
51
4.8.2.3 With ATT as Latent/Intervening Variable.
The path from OU to PEOU or to PU though ATT as intervening variable was
statistically significant. OU to PU through ATT as latent variable was significant; at the
0.05 level (βeta=0.435, T-value =3.629, p<0.05) as indicated on table 4.16 and figure 4.8.
However, OU to PEOU with ATT as latent variable was not significant (p>.05).
4.8.3 Effect of System Accessibility on Adoption of E-learning Technology
The path coefficient for the relationship between SA and adoption of e-learning was weak
and not significant hence dropped from the model.
Table 4.16: Regression Weights
Path Unstandardized
Estimates.
standardized
Estimate SE T
P-
value
Without
latent
PEU <--- OU 0.938 0.881 0.256 3.658 ***
PU <--- OU 0.26 0.236 0.167 3.562 ***
With BI
as latent
BI <--- OU 0.953 0.672 0.18 5.296 ***
PEU <--- BI 0.042 0.056 0.088 0.477 0.633
PU <--- BI 0.143 0.184 0.086 1.658 0.097
With
ATT as
latent
ATT <--- OU 0.899 0.755 0.153 5.869 ***
PU <--- ATT 0.435 0.47 0.12 3.629 ***
PEU <--- ATT -0.075 -0.084 0.125 -0.601 0.548
4.9 Predictive Relevance of the Model
The quality of the structural model can be assessed by R2 which shows the variance in the
endogenous variable that is explained by the exogenous variables. Based on the results
reported in figure 4.8, the R2 was found to be 0.74 and .62: indicating that OU can
account for 74% of adoption for e-learning though PEOU and 62% adoption of e-learning
though PU. The other factors are determined by other variables.
4.10 Chapter Summary
The study results presented and discussed in this chapter reveals the descriptive of SE,
SA, OU as the independent variable, BI, ATT as latent or intervening variables, and lastly
PU and PEOU as dependent variable. The inferential analysis revealed SE and SA had
52
little to no effect on PU and PEOU. Only OU influenced PU and PEOU. Similarly, BI
and ATT as intervening variable only affect OU interaction with PU and PEOU.
53
CHAPTER FIVE
5.0 DICUSSION, CONCLUSIONS AND RECOMMENDATIONS.
5.1 Introduction
This chapter presents the summary of the study, discussion of findings, conclusion and
recommendation. The summary, conclusion was based on chapter four findings while
discussion was based on literature review. The last area on the recommendation covers
the recommendation on the area of study and recommendation on further studies.
5.2 Summary of the Study
The purpose of the study was to investigate the factors affecting the adoption of e-
learning technology in Kenya. This was guided by the following research objectives; To
establish the effect of self-efficacy on adoption of e-learning technology in Kenya, to find
out how objective usability affects adoption of e-learning technology in Kenya and lastly
to investigate the effect of system accessibility on adoption of e-learning technology in
Kenya.
This study used explanatory/hypothesis research design to investigate the factors which
affect adoption of e-learning technology in Kenya. An explanatory research design was fit
to the study because it will helped to ascertain not only the relationship between the
different variables, but measure the effect and strength of each independent variable on
the dependent variable which was the technology adoption. The target population was
social media (Facebook) users in Kenya. According to Internet World Statistics there are
over 6.2 million Facebook subscribers in Kenya as of June 2017. This study focused on
196 social media users in Nairobi who were interested in e-learning or have ever learned
online. They were sampled by geographic cluster, simple random sampling technique
with a focus on Nairobi. Online questionnaire was used by Google form and 95%
responded.
For objective one, on SE, ‘I feel confident finding information on e-learning (online
learning) systems’ was highly rated as strongly agreed at 47.1% and agreed at 39.3%. The
second question ‘I have the necessary skills for using an e-learning (online learning)
system’ was highly rated as strongly agreed at 44.3% and agreed at 40.7%. The
correlation result revealed positive correlation between PEOU with SE (r=0.441, p<0.05)
and PU with SE (r=0.337, p<0.05). On the CFA, there was a strong model equation but
54
on the SEM it was not. The path coefficient for the relationship between SE and adoption
of e-learning was weak and not significant hence dropped from the model. As discussed
in chapter four (figure 4.7), SE was weak due to high SD and was dropped from the
model. Hence SE does not affect adoption of e-learning in Kenya.
For objective two, response on OU was also similar as follows ‘Once I use an e-learning
system, I can easily remember how to navigate it’ was highly rated as strongly agreed at
36.4% and agreed at 46.4% and ‘E-learning (online learning) systems save me time’ was
highly rated as strongly agreed at 34.3% and agreed at 47.1%. However, the question on
‘Most e-learning (online learning) systems are easy to use’ was rated differently with
agreed at 45.0% and neutral at 26.4%. The correlation result revealed positive correlation
between PEOU with OU (r=0.598, p<0.05) and PU with SE (r=0.456, p<0.05). Based on
SEM, the path coefficient for the relationship between OU and adoption of e-learning was
significant in three levels. Without latent/intervening variable, OU and PEOU was
positive and significant at the 0.05 level (βeta=0.938, T-value =3.658, p<0.05). The
positive relationship indicates that one unit increase in OU will result in 0.938 increase in
PEOU. Similarly, relationship between OU and PU was positive and significant at the
0.05 level (βeta=0.26, T-value =3.562, p<0.05). The positive relationship indicates that
one unit increase in OU will result in 0.26 increase in PU. Further, with BI as
latent/intervening variable, the path from OU to PEOU or to PU though BI as intervening
variable was not statistically significant (p> .05) hence concludes BI as latent/intervening
variable does not influence OU to adoption of e-learning technology. Lastly, with ATT as
latent/intervening variable, the path from OU to PEOU or to PU though ATT as
intervening variable was statistically significant. OU to PU through ATT as latent
variable was significant; at the 0.05 level (βeta=0.435, T-value =3.629, p<0.05). Hence
OU affects adoption of e-learning technology.
On last objective on SA, the response were; ‘I can access e-learning (online learning)
systems on my mobile phone’ was highly rated as strongly agreed at 29.8% and agreed at
38.3%. While the question on ‘I have no difficulty accessing and using an e-learning
(online learning) systems in Kenya’ was ranked differently with agreed at 39.4% and
neutral at 26.6%. The correlation result revealed positive correlation between PEOU with
SA (r=0.441, p<0.503) and PU with SA (r=0.514, p<0.05). The path coefficient for the
relationship between SA and adoption of e-learning was weak and not significant hence
dropped from the model.
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5.3. Discussion of the Results
This section interprets the results and findings of the study. It explores some of the
pertinent issues in the discussion of adoption of e-learning technology in Kenya,
highlighting problems and gaps in existing research and recommending areas for further
research theoretical development and field development.
5.3.1. Effect of Self Efficacy on Adaption of E-learning Technology.
The questions for SE were highly ranked as agreed or strongly agreed: ‘I feel confident
finding information on e-learning (online learning) systems’ was highly rated as strongly
agreed at 47.1% and agreed at 39.3%. The second question ‘I have the necessary skills for
using an e-learning (online learning) system’ was highly rated as strongly agreed at
44.3% and agreed at 40.7%. The correlation result revealed positive correlation between
PEOU with SE (r=0.441, p<0.05) and PU with SE (r=0.337, p<0.05). On the CFA, there
was a strong model equation but on the SEM it was not. The path coefficient for the
relationship between SE and adoption of e-learning was weak and not significant hence
dropped from the model. As discussed in chapter four (figure 4.7), SE was weak due to
high SD and was dropped from the model, it has little to no effect on the adoption of e-
learning in Kenya.
This Self efficacy as a factor in my study matched Venkatesh and Davies (1997) analysis
which narrowed down the Technology Adoption Model by focusing on one of the main
determinants of adoption; Perceived ease of use. In this literature they conducted three
experiments to determine if Computer self-efficacy, their study supported the hypothesis
that an individual’s ease of use is anchored to her or his general computer self-efficacy at
all times (Venkatesh & David, 1997). Whereas the factor alone was outweighed by other
stronger factors in my study, the effect on perceived ease of use and Perceived usefulness
was incredibly strong and cannot be dismissed.
Similarly, my research was also in tandem with Hsia, Chang and Tseng (2012) research
on high-tech firms which have implemented e-learning systems and discovered that
computer self-efficacy is an antecedent of perceived ease of use (Hsia, Chang, & Tseng,
2012). In their work they also highlighted behavioral intention as a factor, whereas my
research was not focusing on BI, it still turned out to have some strength in the model.
56
In agreement with Boateng et al. (2016) who investigated the determinants of E-learning
adoption in developing countries, they theorized that findings from developed countries
on ELA (E-Learning Adoption) .Their study revealed that self-efficacy had a direct effect
on PEOU, which reflects accurately in my study as well; self-efficacy has a strong
relationship with PEOU if not an incredibly strong relationship with adoption if taken in
isolation.
Whereas Saade and Kira (2009) concluded that computer self-efficacy does play an
important role in mediating the anxiety-perceived ease of use relationship for learning
management system (e-learning) usage or adoption. This study reflected that whereas
there is a relationship between self-efficacy and perceived ease of use, which was
stronger than the relationship between PU and SE it is not that strongest variable when it
comes to adoption of technology and in this case e-learning technology.
The results of my study were similar to Zainab, Bhatti and Alshagawi (2017), in their
research they concluded that Computer self-efficacy was statistically insignificant
through PEOU, this differed from the conclusions of earlier research by Zainab et.al
(2015). In addition, PEOU had an indirect effect through PU. Therefore, only PU of the
TAM constructs indicated strong predictive strength in e-training adoption.
The results from my study reflected similarly or rather in tandem with Lee (2006) who
showed that mandatory usage of electronic learning system is necessary in technology
adoption- in order to build computer self-efficacy. The target for the research were people
who have interacted with e-learning systems in one way or another, ergo to them where as
self-efficacy is a factor to consider, it is not the most important one. Because of their
interaction self-efficacy levels were high enough for them to be comfortable with e-
learning systems.
My research varied in that even though self- efficacy is a factor to consider, its strength as
a factor on its own was not enough to affect adoption of e-learning technology. One could
argue that the latest generation have a confidence in technology which lacked in prior
decades ergo it is not a factor that is as prevalent.
5.3.2. Effect of Objective Usability on Adoption of E-Learning Technology
The correlation result revealed positive correlation between PEOU with OU (r=0.598,
p<0.05) and PU with SE (r=0.456, p<0.05). Based on SEM, the path coefficient for the
relationship between OU and adoption of e-learning was significant in three levels.
57
Without latent/intervening variable, OU and PEOU was positive and significant at the
0.05 level (βeta=0.938, T-value =3.658, p<0.05). The positive relationship indicates that
one unit increase in OU will result in 0.938 increase in PEOU. Similarly, relationship
between OU and PU was positive and significant at the 0.05 level (βeta=0.26, T-value
=3.562, p<0.05). The positive relationship indicates that one unit increase in OU will
result in 0.26 increase in PU. Further, with BI as latent/intervening variable, the path from
OU to PEOU or to PU though BI as intervening variable was not statistically significant
(p> .05) hence concludes BI as latent/intervening variable does not influence OU to
adoption of e-learning technology. Lastly, with ATT as latent/intervening variable, the
path from OU to PEU or to PU though ATT as intervening variable was statistically
significant. OU to PU through ATT as latent variable was significant; at the 0.05 level
(βeta=0.435, T-value =3.629, p<0.05). Hence OU affects adoption of e-learning
technology.
As discussed in the literature review, objective usability concerns aspects of the
interaction not dependent on users’ (Hornbaek, 2006) basically, objective usability is
independent of user experience and this can be measured to give a broader and clearer
picture.
This study reflected similarly to the studies of Venkatesh and Davies (1997) and their
contemporaries who highlighted objective usability as a key factor in the adoption of
technology. As per the research above there is a clear relationship between objective
usability, Perceived Usefulness and Perceived Ease of Use.
My study reflected results similar to Zaharias and poulymenakou (2006), research which
studied the essential relationship and interplay between usability and instructional design.
In this literature they highlight the factor of usability with a focus not only on the system,
but the characteristics. This mirrors my results with over 80% of respondents either
strongly agreeing of agreeing to the objective usability queries. The Objective usability
was not only measuring their usability, but ease of system usability.
It was interesting to see the results of the analysis and the strength of objective usability.
This reflected similarity to the conclusion of Shackel (2009) that as computers become
cheaper and more powerful, it seems certain that usability factors will become more and
more dominant in the acceptability decisions made by users and purchasers. In this
58
research Shackel highlighted the importance not only of the objective computer usability
but of the design and user interface and my research added strength to this conclusion.
It was interesting to discover the strong relationship between the latent variable of attitude
and objective usability to PEOU and PU. This was similar to the work by Shackel (2009),
he highlighted the usability variables as; effectiveness, learnability, flexibility and
attitude.
In tandem with Park and Wentling (2007) whose focus was particularly computer
attitudes and computer usability, my research unveiled a strong relationship between
objective usability and adoption and objective usability through PEOU to attitude.
My decision to research on objective usability varied from the work of Hennessy et al.
(2010) who theorized that research on adoption of technology is East Africa needs to
focus on issues like internet access and technology gaps. My work filled this gap by
proving that objective usability is a viable and important factor to consider when it comes
to adoption of E-learning technology.
Objective usability came out very strongly as a factor which affects the adoption of e-
learning technology in Kenya; this filled the gap in African literature, not only focusing
on infrastructural issues but the actual system interaction. This study reflected on the state
of e-learning technology adoption in Kenya as well as the user trend and the importance
of objective usability.
5.3.3. Effect of System Accessibility on Adoption of E-learning Technology
On the last objective of SA, the descriptive response had variance response; ‘I can access
e-learning (online learning) systems on my mobile phone’ was highly rated as strongly
agreed at 29.8% and agreed at 38.3%. While the question on ‘I have no difficulty
accessing and using an e-learning (online learning) systems in Kenya’ was ranked
differently with agreed at 39.4% and neutral at 26.6%. The correlation result revealed
positive correlation between PEOU with SA (r=0.441, p<0.503) and PU with SA
(r=0.514, p<0.05). The path coefficient for the relationship between SA and adoption of
e-learning was weak and not significant hence dropped from the model.
System accessibility refers to a situation where anyone, regardless of their personal
characteristics and type of environment, is able to access the information provided
through the Learning Objects.(Batanero, Karhu, Holvikivi, Oton, & Amado-Salvatierra,
59
2014). My study resulted in a confirmation in the relationship between SA and PEOU as
well as PU but the correlation was very weak, which means that system accessibility has a
positive relationship with these factors but not strong enough to affect the adoption of e-
learning technology in Kenya.
In this study the System accessibility factor focused on two areas, ease of access as well
as access through mobile technology, this was similar to Park, Nam and Cha (2011) who
focused on the behavioral intention to use m-learning technology by university students in
institutions of higher learning. They also reflected positive correlation between SA and
PEOU and SA and PU.
Similarly my work reflected the work of Male and Pattinson (2011) whose research
touched not only on computer usability but mobile technology usability; m-learning. In
the research it was determined that it is essential that e-learning systems are designed to
assist learners or users achieve their societal aspirations not just emphasizing on
successful utilization of the technology, because in these cases success is measured by the
usability. Mobile e-learning was an important question in my research because it not only
reflects on usability but access as well. Over 68% of respondents agreed and strongly
disagrees that they can access e-learning on their mobile phones.
My research differed from Calisir et al. (2014) who focused on e-learning adoption for
blue collar employees in an automotive industry. SA for E-learning in my study was
broadly covered and not specific to an industry. Regardless there were still similar
formative conclusions that access is an important factor to consider. System accessibility
was not strong enough to hold to the SEM structure but there was still a relationship
between SA and PEOU and SA and PU.
This research differed from those in the literature review especially in regards to the
strength of the factor of system accessibility. There was a relationship between the PEOU
and PU but it was not strong enough to hold to the model, which means the significance
of SA in adoption of e-learning technology in Kenya is little to none.
5.4. Conclusions
5.4.1. Effect of Self –Efficacy of Adaption of E-Learning Technology
The path coefficient for the relationship between SE and adoption of e-learning was weak
and not significant hence dropped from the model. As discussed in chapter four, SE was
60
weak due to high standard deviation and was dropped from the model. Ergo as per the
research it is not an important factor adoption of e-learning in Kenya with or without BI
and ATT as intervening variables. This does not nullify the contribution of this factor, but
in comparison to other factors its strength is lessened.
5.4.2. Effect of Objective Usability of Adaption of E-Learning Technology
Based on SEM, the path coefficient for the relationship between Objective Usability and
adoption of e-learning, it is clear that objective usability affects adoption of e-learning
technology in Kenya significantly.
5.4.3. Effect of System Accessibility of Adaption of E-Learning Technology
SEM on SA could not be performed because the path coefficient for the relationship
between SA and adoption of e-learning was weak and not significant hence dropped from
the model.This concludes SA is not an important factor on adoption of e-learning
technology without BI and ATT as intervening variables. System accessibility factors are
lined through the two comparison variables of objective usability and self –efficacy but
alone is rather weak. It is not a significant.
5.5 Recommendations
5.5.1. Suggestions for Improvement
From the results of this study there are several proposed improvements that can be
undertaken to boost the adoption of e-learning practices in Kenya.
5.5.1.1 Effect of Self –Efficacy of Adaption of E-learning Technology
Self- efficacy in regards to technology has increased at an incredible rate and this is
mainly due to cheaper devices and internet penetration. The government needs to
continue supporting the initiatives which encourage the continuous use of ICT to improve
the already growing levels of self-efficacy, and enforce policy to support the growing
industry as well as subsidies to encourage more adoption. Potential instructors need to
take advantage of the populations’ steady self-efficacy growth to create content which can
be consumed on e-learning platforms. As people continue to interact with technology so
is the way in which they will want to learn. E-learning platforms need to take advantage
of the fact that it is not confidence-Self –efficacy which affects its adoption and leverage
the environment to not only facilitate content creation but publishing on their sites.
61
5.5.1.2 Effect of Objective Usability of Adaption of E-learning Technology
Objective usability is integral to the adoption of E-learning technology. The Kenyan
government needs to set up standards and regulations which can set ISO standards for e-
learning technology in order to ensure user experience is up to quality to ensure objective
usability is positive and thus people utilize the technology efficiently. Objective usability
does not stop at the technology but the content as well. Potential instructors need to keep
this in mind and make their content interactive and interesting to maintain interest and
user adoptability. E-learning platforms need to stream line their user experience journey
to ensure that people come back to use their platforms and interact with their system.
Objective usability allows for a blank slate initial interaction, after which the experience
determines whether people will return to the platforms.
5.5.1.3 Effect of System Accessibility on Adaption of E-learning Technology
In this study system accessibility has little to no effect on the adoption of e-learning
adoption in Kenya, but we must take into consideration the study focused on Nairobi,
which has a high penetration of internet and access to exposure and knowledge of
technology. In this case accessibility is not an important factor, but in rural areas the
government needs to kick start initiatives to bring them up to the level of the metropolitan
areas. E-learning platforms need to work hand in hand with government to create projects
which can loop in the rural areas and customize their platforms to suit offline access.
5.5.2. Suggestions for Further Research
Based the responses received and the SEM analysis further research should focus on the
two variables of system accessibility and self-efficacy involving a larger number of
variables to examine as well as larger numbers of respondents to support the factor
analysis. Research should also focus on attitude and behavioral intention and their
interaction with perceived eases of use and perceived usefulness. The research needs to
now shift from the metropolitan areas to the rural areas in order to get a holistic view of
the e-learning environment in Kenya.
62
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APPENDICES
Appendix I: Questionnaire
SECTION I: GENERAL INFORMATION
Kindly answer all the questions by ticking in the relevant box or answer.
1. Gender? Male Female
2. Age? Less than 18 18-25 26-32 33-40 Above 40
3. Status? Single Married
4. Educational Background.
Below high school High school Undergraduate Postgraduate
5. Have you ever learned anything online? Yes No
6. Which platform did you use?
Facebook YouTube Google Udemy Zydii Coursera
Other e-learning platform
7 . What skill/s did you want to learn?
Career Relationship Business Educational
Other
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SECTION 2: TECHNOLOGY ADOPTION
Please indicate the extent to which you agree or disagree with the following statements by
circling the relevant number where; 1=Strongly Disagree, 2=Disagree, 3= Neutral,
4=Agree, 5=Strongly Agree).
Please indicate the extent to which you agree or disagree with the following statements by
circling the relevant number where; 1=Strongly Disagree, 2=Disagree, 3= Neutral,
4=Agree, 5=Strongly Agree).
E-learning Self –Efficacy
SE1 I feel confident finding information on e-learning (online
learning) systems. 1 2 3 4 5
SE2 I have the necessary skills for using an e-learning (online
learning) system 1 2 3 4 5
Objective Usability
OU1 Most e-learning (online learning) systems are easy to use 1 2 3 4 5
OU2 Once I use an e-learning system, I can easily remember how to
navigate it. 1 2 3 4 5
OU3 E-learning (online learning) systems save me time. 1 2 3 4 5
73
Please indicate the extent to which you agree or disagree with the following statements by
circling the relevant number where; 1=Strongly Disagree, 2=Disagree, 3= Neutral,
4=Agree, 5=Strongly Agree).
System Accessibility
SA1 I have no difficulty accessing and using an e-learning
(online learning) system in Kenya. 1 2 3 4 5
SA2 I can access e-learning (online learning) systems on my
mobile phone 1 2 3 4 5
Please indicate the extent to which you agree or disagree with the following statements by
circling the relevant number where; 1=Strongly Disagree, 2=Disagree, 3= Neutral,
4=Agree, 5=Strongly Agree)
Perceived Ease of Use
PEU1 I find e-learning (online learning) systems easy to use.
1 2 3 4 5
PEU2 Learning how to use an e-learning system is easy for me.
1 2 3 4 5
PEU3 It is easy to become an expert at using an e-learning (online
learning) system.
1 2 3 4 5
74
Please indicate the extent to which you agree or disagree with the following statements by
circling the relevant number where; 1=Strongly Disagree, 2=Disagree, 3= Neutral,
4=Agree, 5=Strongly Agree)
Please indicate the extent to which you agree or disagree with the following statements by
circling the relevant number where; 1=Strongly Disagree, 2=Disagree, 3= Neutral,
4=Agree, 5=Strongly Agree)
Perceived Usefulness
PU1 E-learning (online learning) would improve my learning
experience. 1 2 3 4 5
PU2 I can us e-learning (online learning) to increase my
personal and professional skills. 1 2 3 4 5
PU3 E-learning could make it easier to study course content
(instead of physical classes). 1 2 3 4 5
Attitude
ATT1 Studying through e-learning is a good idea. 1 2 3 4 5
ATT2 Studying through e-learning is a sensible idea 1 2 3 4 5
ATT3 I have positive thoughts toward e-learning 1 2 3 4 5
75
Please indicate the extent to which you agree or disagree with the following statements by
circling the relevant number where; 1=Strongly Disagree, 2=Disagree, 3= Neutral,
4=Agree, 5=Strongly Agree).
Behavioral Intention
BI1 I intend to check announcements from e-learning (online
learning) systems frequently. 1 2 3 4 5
BI2 I intend use e-learning (online learning) systems quite a bit
1 2 3 4 5
THANK YOU
